5350 results about "Power management" patented technology

XCB poly-radio devices as finders, locators, scanners, sensors, and Bluetooth (BT) radio topology reporters as part of a global lost-and-found services system. XCB devices function as “smart objects” and combine dual transceivers operating in the telecommunications and the low power ISM radio spectra, with default power management overrides under local control of the BT modem. “Familiar zones” are defined, and a “familiarity index” versus a “strangeness index” based on meta-analysis of BT radio contact logs are used to assign location. Location, in concert with other sensor data, is coupled to conditional logic rules programmable by the user or the system administrator. Locations, determined by monitoring familiar and unfamiliar BT radio topology, trigger executable commands to smartphones, to smart devices generally, and to remote machines, and can for example, generate alerts to a user if radiotagged assets, children or pets have strayed, are lost, or are left behind. When tied to cloud predictive services or increased edge computing capacity, incipient loss of radiotag objects can be detected before an owner knows the smart object is lost. In another embodiment, XCB devices having a novel chip architecture for improved power management of cellular and MCU circuits mediated by BT radio modem that achieves “situational awareness” by monitoring the BT signal environment around the device.

The invention discloses a power distribution management system of an intelligent charging pile, and belongs to the technical field of electric vehicle charging facilities and intelligent power grids. The load prediction module carries out space-time alignment fusion on historical data and real-time monitoring data to generate a power distribution demand prediction value, and the edge calculation controller executes a model prediction control algorithm based on multi-source data to generate a real-time control strategy containing relay time sequence parameters and a capacitance compensation scheme. And the dynamic power distribution adjustment module executes strategy parameters through the solid-state relay array and the parallel compensation capacitor bank. Self-adaptive adjustment of the power distribution network is achieved through a real-time monitoring-prediction-optimization closed-loop control mechanism, the harmonic content of the power grid is effectively reduced, the energy distribution efficiency of the charging pile group is improved, and the method is suitable for intelligent electric energy management of public charging stations and other scenes.

The invention discloses a non-energy-storage off-grid photovoltaic hydrogen production system and a control method. The control method comprises five steps of environmental data acquisition and preprocessing, photovoltaic cell output power estimation, electrolytic cell power management, converter control parameter optimization and system performance evaluation and adaptive adjustment. The method comprises the following steps: firstly, processing environmental data by adopting an isolation forest algorithm, Kriging interpolation and an empirical mode decomposition method, and extracting time sequence features; and then combining with the estimated photovoltaic output power. And then the genetic algorithm and dynamic programming are utilized to realize electrolytic cell power optimization distribution. And then converter control parameters are optimized through subspace identification, pole assignment and a hybrid optimization algorithm. And finally, system performance evaluation and adaptive adjustment are realized by adopting a sliding time window method, singular spectrum analysis and a fuzzy prediction control method. The technical problems of the energy-storage-free photovoltaic hydrogen production system in the aspects of environmental data processing, power estimation, energy management, control optimization and the like are solved, and the operation efficiency and reliability of the system are improved.

The invention relates to the technical field of electric power government affair management, and provides a cross-department government affair big data business co-processing method based on a computing power platform and data fusion, comprising the following steps: S1, deploying a heterogeneous computing power cluster, and establishing a computing power resource dynamic scheduling data processing platform comprising a CPU, a GPU, an FPGA and an edge computing node; s2, acquiring multi-dimensional data of power grid equipment data, power grid load data, industry power consumption data and government policy data; and S3, designing a cross-department data access system based on the zero-trust architecture, and developing a multi-dimensional model of a power grid risk prediction model, an industry energy consumption analysis model and a carbon emission accounting model in power management. The space-time joint probability prediction and conflict priority ranking algorithm is applied to power-government affair emergency disposal, intellectualization and precision of cross-department business collaboration are achieved, power faults with serious consequences are rapidly handled according to the priority level, and the harmfulness and unpredictability of the power faults can be reduced.

The invention relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

The invention provides a self-adaptive power supply energy-saving configuration method and system based on energy gradient utilization, and relates to the technical field of power supply management, and the method comprises the steps: collecting the operation parameters of electric equipment, building a power utilization model, and carrying out the self-adaptive correction; constructing a multi-stage energy distribution matrix according to the corrected power utilization model, and performing energy efficiency grading on the power utilization equipment; performing spatial-temporal feature extraction by adopting a bidirectional attention mechanism, generating an electric equipment priority strategy in combination with a self-calibration network, and further calculating an energy distribution proportion of each level of electric equipment; and finally, generating a control signal according to the energy distribution proportion, adjusting the power supply power of each stage of electric equipment, distributing the exceeded power to the electric equipment with the secondary high energy conversion efficiency when the power value exceeds a target power threshold value, and outputting a self-adaptive energy balance control instruction according to the prediction control model to adjust the operation state of the electric equipment.

The invention relates to the technical field of data management, in particular to a low-altitude economic flight data management method and system based on edge computing, and the system comprises edge computing nodes deployed on an aircraft and a ground base station, a sensor for collecting multi-source data, a module for preprocessing the data, and an annular data queue caching mechanism. The system comprises a composite biological feature code generation module, a distributed storage edge node, an abnormal fluctuation feature extraction module, an equipment health state and trend analysis and calculation module, a virtual power supply manager, a main power supply, a standby power supply, an abnormal behavior monitoring module and a defense strategy library. After preprocessing and caching, feature codes are generated and encrypted distributed storage is carried out, dynamic resource adjustment is realized through state evaluation and a digital twinning and optimization algorithm, a neural network is utilized to manage a power supply, data security is guaranteed in combination with abnormal monitoring, and a whole-process management system is formed.

The invention provides a multifunctional integrated cleaning robot for a photovoltaic module of a power station, which belongs to the technical field of photovoltaic module cleaning and comprises a control chip, a robot body device, an automatic cleaning device, a dust collection device, a water spraying system device, a solar charging device, an intelligent navigation device, an environment monitoring device, a communication interface device and a power management device. An intelligent cleaning control module is arranged in the control chip. The method comprises the following steps: constructing a three-dimensional map through environment perception; evaluating the pollution state of the photovoltaic module by using a sensor to generate a thermodynamic diagram; a double-layer optimization model is adopted for path planning and resource allocation, an upper layer adopts an improved ant colony algorithm to take charge of a global path, and a lower layer optimizes cleaning parameters based on a particle swarm algorithm; cleaning operation is executed, and real-time monitoring is carried out; evaluating the cleaning effect; water resource and energy intelligent management is carried out; multi-machine collaboration and task allocation are realized; processing an abnormal condition; the cleaning strategy is continuously optimized through machine learning, and efficient and accurate photovoltaic module cleaning is achieved.

The invention relates to the technical field of power management, in particular to an intelligent computing resource energy-saving scheduling system based on load prediction, which comprises a cache access monitoring module, a burst load judgment module, a frequency fluctuation analysis module, a load classification and identification module and an intelligent regulation and control strategy module. According to the method, the access frequency fluctuation is analyzed by monitoring the cache access data, the access mode change is identified, the load judgment precision is improved, the burst load identification is enhanced in combination with the hit rate reduction range, the resource scheduling is more accurate, the CPU / GPU operation frequency and the fluctuation rate are monitored, the abnormal fluctuation is identified, the calculation resource allocation is optimized, and the energy consumption waste of frequency adjustment is reduced; load classification, resource matching degree optimization, dynamic adjustment of CPU / GPU computing power and memory allocation, coordination of computing and data transmission, optimization of energy consumption management, guarantee of task stability and reduction of idle and unbalanced resource problems are carried out based on a time proportion and task characteristics.

The invention discloses a 5G base station power supply state intelligent monitoring system and method, and belongs to the technical field of intelligent monitoring. According to the system, operation parameters of a 5G base station power supply are acquired in real time through the data acquisition module, and after the operation parameters are processed by the signal conditioning and conversion module, the master control processing unit analyzes digital signals in real time, detects abnormal states and generates alarm information. The communication module is responsible for data transmission and instruction receiving, and the power management and safety protection module provides overvoltage, undervoltage, overcurrent and short circuit protection and triggers emergency measures. The artificial intelligence analysis module performs fault prediction and performance optimization based on historical data and real-time data, and the remote monitoring terminal realizes data visual monitoring and management. According to the invention, the real-time performance and accuracy of monitoring are improved, unified management of multiple base stations is realized, the operation and maintenance cost is reduced, and stable operation of a 5G communication network is guaranteed.

Embodiments pertain to reducing power consumption in a computing system comprising one or more deterministic processors. A controller generates a plurality of control signals for a voltage regulator to regulate a supply voltage of a respective one of the one or more deterministic processors. A power management module determines an initial profile for power consumption and performance of an algorithm executed on the respective deterministic processor having an initial value for the supply voltage and an initial value for a clock frequency. The power management module further determines a target profile for power consumption and performance of the algorithm executed on the respective deterministic processor. The controller modifies the plurality of control signals based on the initial profile and the target profile. The respective deterministic processor executes the algorithm while the supply voltage is dynamically modified by the voltage regulator based on the modified plurality of control signals.

The invention provides a power management chip high-low side driving signal time sequence optimization method and system. The method comprises the following steps: acquiring temperature data of a power management chip under a switch switching transient working condition through an infrared thermal imaging technology, and synchronously acquiring three-dimensional temperature field distribution of junction temperatures on the surface and inside a packaging layer; based on the space continuity of a temperature field, extracting the temperature change characteristics of a power device area, converting the temperature gradient into Seebeck voltage by using a thermoelectric material embedded in a hot spot area, and constructing a dynamic thermoelectric feedback network; according to Seebeck voltage data, a coupling relation between hot carrier concentration and driving signal phase deviation is established, and disturbance of hot carrier mobility change on MOSFET switching delay is quantified; the rising / falling edge slope of the grid voltage is adjusted based on the coupling relation, the phase deviation caused by the hot carrier effect is eliminated through dynamic compensation, and the switching performance is optimized. The dynamic response precision of the power management chip is improved.

The invention relates to an intelligent self-adaptive gas sensor adjustment controller and a working method thereof, and belongs to the technical field of gas detection. The adjustment controller comprises a main control module, a gas detection module, an environmental parameter module, an adjustment execution module, a communication module and a power management module, the main control module compensates detection parameters obtained by the gas detection module according to environmental parameter information obtained by the environmental parameter module in combination with built-in standard parameters and a compensation algorithm, and the adjustment execution module performs adjustment according to the compensation parameters. And whether the calibration execution module is started to calibrate the gas detection module is determined by judging the output stability of the gas detection module. According to the control method of the adjustment controller, through intelligent and self-adaptive adjustment control, the measurement precision, stability and applicability of the gas sensor are remarkably improved, and a reliable solution is provided for gas detection in a complex environment.

The invention belongs to the field of early warning and monitoring of power equipment, and particularly discloses an early warning system and method for inclination and collapse of a tower. The system comprises a sensor module, a data acquisition and preprocessing module, an edge calculation module, a data transmission and synchronization module, a central processing and intelligent analysis module, a visualization and early warning module, a power management module, a model training and optimization module and a system management and maintenance module. The system is high in data acquisition precision, strong in system robustness and high in early warning efficiency, can efficiently and accurately carry out real-time monitoring and inclination collapse early warning on the state of the tower of the transformer substation, and remarkably improves the operation safety and management efficiency of the transformer substation.

The invention relates to the technical field of power management, and provides a power distribution method based on BMS power control and related equipment thereof. The method comprises the following steps of: performing multi-mode synchronous acquisition and self-adaptive preprocessing on detected battery pack global state data to obtain a battery state tensor, and performing unsteady-state signal decomposition and time-space feature extraction on a received load power demand signal to obtain a power component matrix; performing multi-constraint dynamic planning on the battery state tensor and the power component matrix to obtain an initial power distribution strategy, performing dynamic correction in combination with a deep reinforcement learning model to obtain an adaptive distribution strategy, and performing multi-layer circuit equalization optimization on the adaptive distribution strategy to obtain a power regulation instruction set; and finally, performing multi-physics field coupling verification and fault-tolerant reconstruction on the power regulation instruction set to obtain an execution signal sequence. According to the invention, through data fusion, demand prediction, strategy optimization and deep learning, power distribution is accurately regulated and controlled according to the battery state and the load power demand.

The invention provides a power management chip pin state intelligent detection method and system. The method comprises the steps that temperature difference quantity is input into a gradient heat conduction compensation structure, and compensation current opposite to a temperature gradient vector is generated based on temperature field distribution in the copper-based composite material; superposing and coupling the voltage fluctuation signal and the compensation current to generate a reference voltage signal; performing frequency component separation processing on the reference voltage signal, and extracting electromagnetic interference characteristic data in a frequency component; performing time sequence correlation on the electromagnetic interference characteristic data and the temperature distribution data, dynamically adjusting an interference suppression parameter through closed-loop control, and acting the adjusted interference suppression parameter on an interference suppression process of a reference voltage signal to generate a pin state detection signal; and detecting the pin state of the power management chip according to the pin state detection signal. According to the invention, the pin voltage stability and the state detection precision of the power management chip are improved.

The invention relates to the technical field of substation control, in particular to a substation control system based on cloud control and a local area network. Comprising a substation data acquisition module, a data analysis module, a cloud cooperative control module, an edge calculation response module and a safety guarantee module. The substation data acquisition module acquires multi-dimensional data through a local area network, and the data analysis module identifies abnormal and fault modes by adopting a dynamic multivariate autoregression tree algorithm. The cloud cooperative control module performs global load scheduling by using a sparse distributed optimization algorithm, and the edge calculation response module realizes rapid local response control based on a Markov random field model. And the safety guarantee module is combined with deep learning and a multi-factor identity authentication technology to ensure data transmission safety. According to the invention, the automatic control level and the overall operation efficiency of the transformer substation are improved, and efficient, stable and safe intelligent power management is realized.

The invention relates to the technical field of power systems, and discloses an industrial park distributed power utilization network simulation analysis power utilization behavior evaluation method comprising the following steps: S1, data acquisition: power load data, equipment operation state data and environment data of each power utilization unit in an industrial park are acquired in real time, and the environment data comprise temperature and humidity; s2, data preprocessing: preprocessing the collected data to obtain complete data; and S3, simulation modeling: establishing a power load flow model based on the park information and the real-time data, and performing simulation modeling to generate a dynamic model under various power consumption situations. According to the invention, through real-time data acquisition, dynamic load scheduling and a deep reinforcement learning algorithm, flexible optimal distribution of electric power resources is realized, the efficiency and stability of park electric power management are improved, stable operation of equipment is ensured, and energy waste is reduced.

The invention provides a virtual power plant energy storage system co-scheduling and control method and system, and relates to the technical field of power management, and the method comprises the steps: obtaining power grid scheduling data, calculating the charge and discharge income, collecting the real-time parameters of a battery, analyzing the performance attenuation law through deep reinforcement learning, determining the initial working parameters, predicting the system state based on a rolling time domain, and obtaining the real-time parameters of the battery. An optimal scheduling scheme is calculated through mixed integer linear programming and model prediction control in combination with a power distribution strategy and is corrected in real time, operation is executed according to a time-phased scheduling instruction and is monitored in real time, and an emergency strategy is started when necessary, so that the economic benefit and the safety of an energy storage system are improved, and the service life of a battery is prolonged.

A method for battery power management based on battery nonlinearity characterization and compensation using a nonlinear resistor-capacitor (RC) equivalent circuit model (ECM) may include: measuring a current, a terminal voltage, and a temperature of a battery; implementing the nonlinear RC ECM comprising fixed, logarithmically spaced time constants and current dependent resistances and capacitances; constraining resistances of the nonlinear RC ECM to be nonnegative and performing a nonlinearity characterization procedure using the nonlinear RC ECM configured to characterize variations of a battery impedance with current amplitudes over a range of frequencies and create ECM parameter variations with current amplitudes by fitting impedance variations by nonlinear RC ECMs; performing online estimation of the nonlinear RC ECM using a battery current and a terminal voltage of the battery over time; and modifying the online estimated nonlinear RC ECM using ECM parameter variations to compensate for nonlinearity of the battery.

The invention particularly relates to a lead-acid battery SOC accurate calculation and lead-acid battery polymorphic anomaly detection and protection method, which comprises the following steps: firstly, collecting and preprocessing multi-dimensional data information of a lead-acid battery, and then respectively calculating the preprocessed data based on a preset ampere-hour integral method and an improved extended Kalman filtering method; meanwhile, the battery operation condition is recognized through a preset condition recognition algorithm, and then dynamic weight fusion is conducted on calculation results of the two methods according to the condition to obtain a first fusion result; and then temperature compensation correction is performed on the result by using a preset temperature compensation model, and finally correction is performed in sequence by using a preset internal resistance-SOC relationship and an aging correction model to obtain a first SOC value. Through multi-dimensional data fusion, dynamic working condition weight distribution, temperature compensation and multi-mode cooperative protection, the SOC estimation error of the lead-acid battery is reduced, the safety and environmental adaptability under complex working conditions are improved, and meanwhile, intelligent interaction and low-power-consumption management are supported.

The invention relates to the technical field of power management, in particular to a multi-source heterogeneous data three-dimensional fusion analysis method based on a power grid unified information model, which comprises the following steps of: performing timestamp alignment and space coordinate normalization processing on SCADA system real-time data, GIS geographic information data, equipment vibration spectrum data and infrared thermal imaging data; and constructing a unified three-dimensional attribute matrix, and fusing the multi-source data based on a power grid unified information model. A three-dimensional space interpolation algorithm is adopted to carry out topological reconstruction on the model, a three-dimensional space-time topological structure containing equipment connection relations, electrical parameters and space coordinates is generated, intelligent prediction is carried out on the equipment fault probability in combination with a depth prediction model, and an augmented reality display interface with prediction and early warning information is generated through a three-dimensional visualization engine. According to the invention, the accuracy and the real-time performance of power grid equipment monitoring are effectively improved, the fault early warning and decision support capability is enhanced, and the operation safety and the management efficiency of a power grid are improved.

The invention relates to the technical field of charging power management of charging piles, and particularly discloses a charging power distribution method and device for a multi-gun charging pile, and the method comprises the steps: calculating the theoretical power of each charging gun through collecting the battery information, including the current electric quantity, the battery capacity, the rated charging power and the charging demand, of each vehicle in real time; the safety and the stability are comprehensively evaluated according to the dynamic change of the battery temperature and the load rate of the charging gun; according to the method, the charging power can be dynamically adjusted, the safety and the high efficiency of the charging process are ensured, and the problems of battery damage and unstable charging caused by improper power distribution are avoided; the invention further provides a real-time monitoring interface and an alarm mechanism, abnormal conditions can be found and processed in time, and it is ensured that the charging process is carried out smoothly.

The invention relates to the field of energy storage power supply management, in particular to a power supply management method, system and equipment based on a household energy storage power supply system. The method comprises the following steps: collecting a household power utilization monitoring data stream, carrying out equipment use behavior preference mining and multi-scale state transition deep learning, and generating a user equipment behavior prediction model; mining power utilization behaviors of power utilization terminals one by one based on the household power utilization monitoring data flow, performing multi-time-point power utilization demand prediction based on a user equipment behavior prediction model, and constructing a multi-time-point power utilization demand prediction map of the user; and obtaining meteorological environment parameters of a current user family area, performing climate disturbance demand adjustment on the power demand prediction maps at the multiple time points based on the meteorological environment parameters, and constructing a climate disturbance power demand prediction map. According to the invention, through dynamic user demand changes, the energy efficiency management level and the energy allocation precision of long-term operation of the energy storage system are improved.

The invention belongs to the technical field of intelligent hardware control methods, and discloses a low-power-consumption intelligent hardware control method which comprises the following steps: acquiring working state parameters and user interaction information of intelligent hardware, and generating an equipment state feature vector; judging whether a dormancy condition is met or not according to the equipment state feature vector; when the sleep condition is satisfied, sending a network communication agent instruction; the coprocessor configures an internal timer and awakens signal output after receiving the instruction; the master control stores a system operation state, configures a wake-up source and then enters a deep sleep state; the coprocessor cuts off the power supply of the inactive module; when a timer of the coprocessor reaches a preset network heartbeat cycle, a wake-up signal is sent to the master controller; and the master control recovers the system state, updates the feature vector and re-decides whether to sleep or not. And through multi-dimensional state perception and a master-slave cooperation architecture, and through refined power management and a dynamic reevaluation mechanism, the equipment power consumption is obviously reduced on the premise of ensuring the user experience.

The invention provides a charging pile supporting multi-point automatic cooling, and belongs to the technical field of charging piles, and the charging pile comprises a main control module, a temperature acquisition module, a current acquisition module, a pressure detection module, a heat dissipation control module, a communication module, a storage module, a display module and a power management module. A system control module arranged in a main control module firstly acquires temperature data of each point of a charging pile through a temperature acquisition module, constructs a temperature difference matrix and performs matrix decomposition to obtain a temperature space basis matrix and a time characteristic matrix; then, a dynamic convolution kernel is generated based on the characteristic value of the temperature space basis matrix, a temperature characteristic extraction network is constructed, and accurate extraction of temperature characteristics is realized. And the system further establishes a thermodynamic state equation set, the heat dissipation power is accurately calculated and controlled in combination with a temperature difference change coefficient matrix, and finally accurate regulation and control of multi-point temperature are achieved.

The invention discloses a single-chip microcomputer hierarchical dormancy control method based on dynamic power consumption prediction, and relates to the technical field of single-chip microcomputer low power consumption control, and the method specifically comprises the steps: building a mapping relation between a load current and a clock frequency; setting a dynamic dormancy threshold value; constructing a dynamic power consumption prediction model; a dormancy strategy is designed, module-level power consumption management and single-chip microcomputer function-based power consumption management are adopted, the callable frequency or the maximum operation duration of each function is distributed according to task priorities and battery residual energy consumption, and single-chip microcomputer peripherals are triggered through events and are independently awakened; and executing the sleep strategy, automatically awakening the single chip microcomputer according to a preset awakening condition, and realizing abnormity positioning and processing based on threshold comparison and power consumption monitoring in the process of executing the sleep strategy. According to the invention, module-level power consumption management and single-chip microcomputer function-based power consumption management are realized, the limitation of traditional global dormancy is broken, the need of overall reset during wakeup is avoided, and the response speed is improved while the energy consumption is reduced.

Described are concepts, systems, system architectures, circuits, methods, and techniques directed toward power management and control. In particular, described are concepts, systems, system architectures, circuits, methods, and techniques for implementing power converters that may not have a right-hand pole zero in their linearized, averaged control-to-output transfer function, but that may still have buck and boost functionality.

The invention discloses a cleaning robot navigation system, which comprises a central control unit, a sensor module, a motion control system, a cleaning assembly, a communication assembly, a safety and diagnosis module and a battery and power supply management module, and is characterized in that the sensor module comprises a laser radar, an ultrasonic sensor, a visual camera and an inertial measurement unit; the laser radar is used for environment scanning and obstacle detection, the ultrasonic sensor is used for close-range obstacle detection, the visual camera is used for image processing and ground analysis, and the inertial measurement unit is used for attitude and position detection. A series of innovative technologies such as advanced sensor technology, data fusion, path planning, dynamic obstacle avoidance, cleaning task optimization and the like are combined, and a plurality of problems faced by an existing cleaning robot are solved. According to the technical scheme, the cleaning robot can be remarkably improved in the aspects of environment perception, navigation precision, cleaning efficiency, task optimization and the like.

The invention belongs to the technical field of power supply management, and particularly relates to a high-precision DCDC power supply voltage stabilization calibration system and method, which comprises a DCDC power conversion module, a traditional feedback control module, a high-frequency ripple sampling and digitization module, a dynamic load prediction module, a feedforward compensation generation module and a calibration management module, through high-frequency ripple sampling and digital output of ripple signals, real-time spectrum analysis is carried out to predict load transient changes, prospective compensation signals are generated, and traditional feedback control is combined to finely adjust PWM signals of the DCDC module. According to the scheme, feedback lag is overcome, transient overshoot / undershoot of voltage is remarkably restrained, the dynamic voltage stabilization error is controlled within + / -0.3%, and high-precision voltage stabilization under both static and dynamic working conditions is achieved.