77924 results about "Electric power" patented technology

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

The invention discloses a distribution line fault intelligent diagnosis method based on multi-source information fusion, and belongs to the technical field of power system fault detection and diagnosis, and the method comprises the steps: obtaining distribution line data and meteorological data; superposing the distribution line data with the meteorological data to generate a space-time fusion feature matrix based on the wire space displacement; dynamically adjusting a preset initial threshold value by using the space-time fusion feature matrix; and performing multi-round verification on the abnormal fluctuation of the electrical parameters of the line according to the fault judgment threshold, generating a fault positioning instruction, performing real-time matching with the topological structure of the line, correcting the priority queue of the fault section based on the switching action feedback data of the feeder terminal, and triggering the fault removal action matched with the priority. According to the method, spatial-temporal feature fusion of current, voltage, weather and spatial displacement data and a dynamic threshold adjustment mechanism are adopted, the fault type can be accurately identified, the fault section can be positioned in real time, and the diagnosis precision and the response speed in a complex environment are remarkably improved.

A system for landing and charging an autonomous unmanned aerial vehicle (UAV) is disclosed. The UAV comprises a set of battery powered rotors to fly through the air. The UAV further comprises a landing peg that projects beneath the body configured to be received by a socket of a corresponding landing base. The landing base can have a funnel top to make landing easier. The landing peg has a set of power transfer means that connect with power transfer means in the landing base to recharge the batteries of the UAV. The UAV can be guided to land in the landing base by a ring of LEDs on the landing base.

The invention relates to the technical field of electric power short-term load prediction, in particular to an industrial park short-term load prediction method and system based on a Diffusion-Transform-AR model. Acquiring non-stationary historical load data and meteorological data of the industrial park, and performing missing value processing on the data; performing correlation analysis on the data by using the maximum information coefficient, selecting meteorological data with relatively high correlation, and reprocessing time data; performing time sequence decomposition on the load data by using a seasonal trend decomposition method to obtain trend term, seasonal term and residual term features; constructing a diffusion Transform prediction model, and taking the residual term, the meteorological data and the time data as input prediction residual; constructing an autoregression prediction model and taking the trend term as an input prediction trend; according to the method, the advantages of various models are combined, time sequence features are deeply mined, the prediction precision and the model generalization ability are effectively improved, and the method is suitable for complex industrial park load prediction scenes.

The invention relates to the technical field of electric power design, in particular to an intelligent dispatching system of a transformer area power supply grid. According to the method, a self-adaptive sampling strategy of real-time load fluctuation and distributed energy output characteristics is adopted; when the system detects the rapid change or abnormal fluctuation of the load, the data acquisition frequency is automatically improved, so that the capture of key information is ensured; when the load is stable, the sampling frequency is reduced, and the collection of redundant data is reduced; adaptive sampling can dynamically adjust the acquisition frequency, it is guaranteed that important data are not lost at the critical moment, and meanwhile the system burden is reduced; a hierarchical data processing architecture is adopted, data processing is divided into an edge layer and a cloud layer, the edge layer is responsible for conducting primary processing on data collected in a transformer area, including denoising, feature extraction and the like, and the data quality is guaranteed; only the key data subjected to primary processing is uploaded to a cloud end for data analysis and decision optimization; therefore, the burden of data transmission and processing is reduced, and the real-time response capability of the scheduling system is improved.

An electric motor based system includes an electric motor; an electric motor power controller for controlling the electric motor; a first control unit, adapted to provide first control signals for the electric motor power controller; an electric power storage device adapted to power the electric motor power controller; a first sensor adapted to determine information related to power exchange between the electric power storage device and the electric motor power controller; and a second control unit, wherein the second control unit is adapted to pass the first control signals, received from the first control unit, to the electric motor power controller in a first mode and adapted to provide second control signals for the electric motor power controller in a second mode; and the second control unit inputs and exploits the information.

The invention relates to a high-voltage equipment defect detection method based on deep learning and multispectral image fusion, and relates to the technical field of electric power high-voltage equipment state detection. The method comprises the following steps: acquiring an ultraviolet image, an infrared image and a visible light image of the surface of the high-voltage equipment; carrying out image pixel feature-based fusion processing on the ultraviolet image, the infrared image and the visible light image through an image fusion method; establishing a high-voltage equipment defect detection model, and training the high-voltage equipment defect detection model by using the fused image data to obtain a high-voltage equipment defect identification model based on the YOLO-STrans multispectral fusion network; and inputting the ultraviolet image, the infrared image and the visible light image of the outer surface of the power high-voltage equipment into a high-voltage equipment defect identification model to obtain a fault identification result of the to-be-detected power high-voltage equipment. The method can improve the recognition precision of the extremely early insulation degradation and temperature anomaly defects of the surface of the high-voltage power equipment.

A method for conditioning and maintaining power with predictive load compensation using an uninterruptible power supply system is disclosed. The method includes measuring an electrical input from a primary power supply using analog sensors and transmitting the input through an impedance to introduce a controlled delay. During the delay, input parameters and downstream system effects are determined based on telemetry sensor data. Digital identities are generated for the electrical input and the downstream load and compared to a stored digital identity representing historical conditions. Using the comparison, a system simulation is performed with analog twinning to predict downstream system responses. The predicted response is used to adjust a power converter parameter, modifying the electrical input to an intermediate output that compensates for voltage fluctuations and harmonic distortions. A secondary power supply with a bidirectional high-discharge battery supplements or stores energy as needed, ensuring stable power delivery to the downstream load.

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

The invention discloses a virtual power plant collaborative optimization operation method based on distributed energy, and the method achieves the real-time monitoring of the operation state of a distributed energy resource through the precise modeling of the distributed energy resource, and predicts the change of the power load demand. A multi-objective optimization model and an advanced optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, are utilized, so that the virtual power plant can dynamically adjust energy supply and load demands, and economic benefit maximization, energy efficiency optimization and carbon emission minimization are realized. The method also considers the energy efficiency and economic benefits of the virtual power plant, and improves the comprehensive benefits of the system through comprehensive analysis and optimization. Through effective interaction with the power grid, the virtual power plant can provide peak regulation, frequency modulation and standby services, a demand response mechanism is implemented, and operation of the power grid is further optimized. The method has the beneficial effects of improving the energy utilization efficiency, enhancing the stability of a power grid, optimizing economic benefits, promoting environmental protection, improving the flexibility and adaptability of the system and supporting policies and market mechanisms.

The invention discloses an electric power capital construction process early warning method and system based on a high-precision three-dimensional model, and relates to the technical field of electric power capital construction, and the method comprises the steps: collecting multi-source data of an electric power capital construction site, and constructing a high-precision three-dimensional model comprising equipment space coordinates, construction time sequence labels and environmental parameters; inputting the space-time diagram into a space-time diagram convolutional network, outputting a risk propagation probability matrix of a three-dimensional space and a construction time sequence dimension, and identifying high-risk nodes and associated construction stages; and dynamically marking root cause coordinates and a risk diffusion range in the high-precision three-dimensional model, generating an early warning instruction containing a visual path, a root cause report and a disposal priority, and pushing the early warning instruction to a terminal in real time through an edge node. Based on the hierarchical space-time attention-physical constraint graph convolutional network, the inter-node cross-stage association weight is dynamically allocated, the convolution kernel parameters are optimized in combination with the physical connection strength, and the space-time association of high-risk node identification is significantly improved.

The present invention relates to the technical field of carbon footprint calculation. Disclosed are a method and system for calculating a carbon footprint of a high-energy-consuming product on the basis of electro-carbon coupling. The method comprises: collecting data of electricity consumption and data of a carbon emission factor for electricity generation in the production process of a high-energy-consuming product; constructing an electro-carbon coupling model for describing the relationship between electricity consumption and carbon emission, and performing iterative optimization; using the electro-carbon coupling model combined with the collected electricity consumption data and the carbon emission factor for electricity within a corresponding time period to calculate carbon emissions of energy directly consumed for electricity generation during each phase; and analyzing and calculating indirect carbon emissions of raw materials and energy consumed during the other indirect stages in the production of the product, and verifying the result. Thus, the relationship between electricity consumption and carbon emission is more accurately reflected, the impact of time and place changes on carbon emission factors can be taken into consideration, and a method for comprehensively evaluating an overall carbon footprint of a product is provided, thereby facilitating more comprehensive understanding and management of the environmental impact of the product.

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

An adapter plug for a power bank includes a housing, electrical connectors, prongs, and an electrical circuit. The electrical connector associated with the housing and are configured to contact an electrical connector of a power bank. The prongs are configured to engage an electrical wall outlet. The electrical circuit is disposed within the housing and electrically connects the prongs to the electrical connectors. The prongs, electrical circuit, and the electrical connector of the housing is configured to deliver electrical power from the electrical wall outlet to the power bank to recharge a battery of the power bank when the electrical connector of the housing contacts the electrical connector of the power bank and when the prongs engage the electrical wall outlet.

The invention discloses a power system load prediction method based on multivariate data fusion, and relates to the technical field of power systems. The problems that in the prior art, multi-source data fusion processing is complex, timeliness and processing efficiency are insufficient, and under the complex working condition, the response capacity to short-term emergencies is weak are solved. By collecting and preprocessing the multi-source data in real time, various factors influencing load changes can be comprehensively considered, the response capability to complex working conditions and emergencies is enhanced, the time sequence window is dynamically adjusted to improve the data validity and prediction performance, the accuracy and reliability of power system load prediction are improved, and the power system load prediction efficiency is improved. According to the method, multiple models are constructed and trained to obtain an accurate load prediction result, intelligent prediction and management of the load of the power system are realized through accurate load prediction, abnormal identification and processing are performed in combination with an emergency database, stable and reliable load prediction is provided for the power system, and thus power resource configuration is optimized.

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention provides an electric power line health state assessment prediction method and system based on big data, and relates to the technical field of electric power line health assessment, and the method comprises the steps: preprocessing historical operation data, generating standardized data, extracting fault features through employing a space-time heterogeneous graph network, constructing a basic hierarchical knowledge base for automatic marking, and carrying out the prediction of the health state of an electric power line. Finally generating a training sample set; a directed acyclic graph is modeled, a main transmission line is identified, risk sections are divided, a knowledge base is optimized, an inspection scheme is adaptively generated based on an inspection strategy model of reinforcement learning, the health state of a power line is evaluated, and a health state report is generated; according to the invention, the fault prediction accuracy and inspection efficiency of the power line are improved.

The invention discloses an industrial and commercial park electrical load power prediction method, and relates to the technical field of electrical load prediction, and the method comprises the steps: obtaining park electrical equipment power data, and obtaining a standardized time sequence power matrix through normalization preprocessing; performing wavelet denoising and empirical mode decomposition on the power matrix, and separating a power trend component from a fluctuation component; constructing a trend prediction sub-model by using a long-short term memory network, and constructing a fluctuation prediction sub-model by using a seasonal ARIMA model; carrying out weighted combination on the two sub-models through an integrated learning method, and constructing a comprehensive prediction model; and finally, based on the historical prediction error, setting an adaptive threshold value for constraint processing, and outputting a final prediction result. According to the method, power data features are adaptively separated through a multi-level decomposition strategy, a dual-model collaborative prediction mechanism is adopted, environmental factors are introduced to improve prediction precision, a self-adaptive correction mechanism is designed to ensure result reliability, and accuracy and stability of park power load prediction are remarkably improved.

The invention provides a multi-machine collaborative distribution network inspection method and system based on digital twinning, and belongs to the technical field of digital twinning. According to the technical scheme, an electric power distribution network framework and base station distribution are mapped to a GIS remote sensing map, a comprehensive digital twinning model is constructed, and an inspection area is delimited based on the comprehensive digital twinning model. By analyzing base station distribution and topographic features in the area, the comprehensive inspection difficulty is determined, and then the inspection area is divided into a plurality of task blocks. And selecting an optimal target nest from the deployed unmanned aerial vehicle nests by adopting a contract net mechanism to execute an inspection task, and generating an optimized inspection route according to the task block and the nest position. And finally, the unmanned aerial vehicle controlled by the target nest completes all inspection tasks according to the planned route. According to the scheme, intelligent scheduling of inspection resources and accurate distribution of tasks are realized, the inspection efficiency of the power distribution network is remarkably improved, the inspection cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power grid.

The invention discloses a low-voltage transformer area examination meter practical training device and a fault simulation control method, and relates to the technical field of electric power practical training. The device aims at the problems of single scene, extensive evaluation and low skill improvement efficiency in traditional electric power practical training. The meter simulation module is compatible with various meters and is matched with a dual-communication protocol to realize efficient data transmission; the intelligent interaction module provides high-quality interaction experience through a high-definition touch screen and low-delay transmission; the data generation module can quickly switch customized scenes, and the fault simulation module can accurately inject various faults; the training management module depends on advanced technologies of knowledge maps, deep learning and fuzzy comprehensive evaluation to realize full-process closed-loop management from project design and operation guidance to evaluation feedback and intelligent optimization; all the modules cooperate efficiently, and the professionality, the accuracy and the efficiency of power failure troubleshooting and operation and maintenance skill training of trainees are remarkably improved.

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

The invention relates to an electric power material intelligent detection method based on multi-modal data fusion, and aims to improve the accuracy and automation level of material state recognition. According to the method, in the electric power material operation or circulation process, multi-modal data such as images, infrared thermal imaging, radio frequency identification, vibration response and environmental parameters are acquired through a unified time index, and a structured time sequence data set is constructed. And after normalization and exception elimination processing, multi-dimensional feature vectors including structural strength, temperature distribution, label continuity and dynamic stability are extracted, and weighted statistics and correlation calculation are executed to generate a comprehensive state index. And further through comparison with a historical reference, identifying an abnormal state according to a deviation threshold value, outputting corresponding labels and feature information, and obtaining material state evaluation and disposal suggestions based on rule reasoning. According to the method, accurate monitoring and abnormal early warning of the electric power materials under the driving of the multi-source data are realized, and the method has good practicability and expansibility.

The invention relates to an optical storage cooperative control optimization method, electronic equipment and a medium. The method comprises the steps of collecting historical energy consumption and photovoltaic data of a park, constructing a photovoltaic electronic model based on the data, performing day-ahead prediction by adopting CNN-BiGRU, and generating a load power and photovoltaic generation power sequence; calculating the electricity consumption and the photovoltaic generating capacity of each time period; setting the capacity of an energy storage module, and calculating the energy storage charging and discharging capacity based on an SOC formula; according to the electricity consumption, the generating capacity and the energy storage capacity, determining a peak section discharge demand; photovoltaic fluctuation constraint is combined, and energy storage charging and discharging power is adjusted to stabilize fluctuation; optimizing load power utilization, photovoltaic power generation and energy storage power scheduling based on the power balance relation; and constructing a revenue maximization objective function, establishing a light storage collaborative optimization model in combination with constraints, and solving an optimal operation scheme of a prediction day. According to the method, photovoltaic power generation errors are reduced, power output is balanced, power grid requirements are met, the photovoltaic consumption rate is improved, healthy operation of the system is guaranteed, and operation benefits are improved.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention relates to an electric power working environment intelligent positioning and risk management and control method based on a three-dimensional model, and the method comprises the following steps: S1, constructing a three-dimensional model of an electric power working environment, and carrying out the spatial layering and equipment marking of the three-dimensional model, and forming a three-dimensional panoramic working image with attributes; s2, marking positions of operating personnel and equipment in real time, and generating dynamic positioning in a power operation scene; s3, recording and predicting the track of the operator, and pre-warning whether the operator enters a safe area or a dangerous area; a camera is used for collecting images of the operating personnel, non-standard behaviors of the operating personnel are recognized in real time through an AI algorithm, and behavior risk indexes of the operating personnel are generated based on detection results; s4, training a risk prediction model based on sensor data, detecting possible risk events, and obtaining an operation risk index of the equipment; and S5, calculating a comprehensive risk index in real time. According to the invention, the comprehensive safety of personnel, equipment and environment is guaranteed while the safety management efficiency is effectively improved.

A battery pack includes a battery housing; a battery module disposed in the battery housing, where the battery module includes multiple battery cells, and at least one of the battery cells is a solid-state battery; and a control circuit disposed in the battery housing and configured to use the battery module to supply the electric power to the power tool. The energy W of the battery pack and the volume V1 of the battery pack satisfy the following: when the energy W is greater than or equal to 200 Wh, the volume V1 is less than or equal to 400 cm3; or when the energy W is greater than or equal to 300 Wh, the volume V1 is less than or equal to 800 cm3; or when the energy W is greater than or equal to 700 Wh, the volume V1 is less than or equal to 2500 cm3.

Systems, methods, and apparatuses for supplying power to at least one power consuming device using a solar panel. The solar panel includes at least two solar modules, an output connector, and a cable. The at least two solar modules are connected via a first electrical harness in a first combination of parallel and / or series to provide a first output voltage and via a second electrical harness in a second combination of parallel and / or series to provide a second output voltage. The at least one power consuming device is preferably a rechargeable battery.

The invention relates to the technical field of power system optimization scheduling, and discloses a multi-load equipment energy consumption management method and device and computer equipment, and the method comprises the steps: obtaining electricity price data, and the energy consumption data, the operation state and the load demand of electric equipment, based on the electricity price data, the energy consumption data, the operation state and the load demand, the priorities of the electric equipment are classified; adopting a preset multi-task learning model to predict the load demand of the electric equipment in a future preset time period; generating a multi-target optimization model based on the priority, the load demand and the electricity price data of the electric equipment, and generating a dynamic power distribution scheme of the electric equipment based on the multi-target optimization model; and obtaining the load demand and the external environment of the electric equipment in real time, and adjusting the dynamic power distribution scheme of the electric equipment based on the load demand and the external environment. According to the method, the energy consumption optimization and the operation efficiency are always kept balanced, and the problem that the energy consumption optimization and the operation efficiency are not balanced is solved.