19941 results about "Electric energy" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based electric energy quality disturbance identification and positioning method, which comprises the steps of constructing an electric energy quality disturbance signal data set, performing segmented preprocessing on electric energy quality disturbance voltage data, enhancing time-frequency joint features and encoding disturbance sensitive areas. And constructing a deep learning model for power quality disturbance identification and positioning, and identifying and positioning the power quality disturbance. According to the invention, through adaptive denoising processing, boundary detection and multi-resolution time-frequency feature extraction, the identification precision and positioning precision of power quality disturbance are significantly improved; self-adaptive wavelet denoising and dynamic segmentation are combined, noise interference is effectively suppressed, and the edge characteristics of voltage sudden change points are kept; according to the dual-task sharing network, disturbance identification and positioning tasks are cooperatively optimized, so that the network can consider disturbance classification and time positioning at the same time; and through Bayesian reasoning, the system can output confidence estimation, provides credibility quantification of identification and positioning results, and effectively improves the reliability of the system.

The invention relates to the technical field of optical measurement and geometric position measurement, in particular to a remote multipoint monitoring system and method for roadbed surface settlement, and the system comprises a plurality of distributed monitoring nodes and a remote data platform; the monitoring node comprises a heterogeneous sensor group and an edge processing module which is connected with the sensor group and is used for carrying out local preprocessing and fusion on multi-source data; the wireless communication module is used for sending the data to a remote data platform; the power supply and maintenance module provides electric energy and supports node cleaning and environment adaptation; the remote data platform comprises a data receiving and storage unit, a data fusion and analysis unit, an early warning unit and a visual user interface. According to the invention, a multi-sensor fusion intelligent monitoring network is constructed, all-weather and high-precision remote monitoring of urban road subgrade settlement and associated crack deformation thereof is realized, settlement trend prediction and early warning are provided through AI analysis, and the initiative and safety guarantee level of urban road maintenance are improved.

The invention relates to an energy management method, device and equipment based on an air conditioning system and a medium. The method comprises the steps that in a preset period, a target cold storage capacity calculation model matched with the current working condition is selected from a plurality of candidate cold storage capacity calculation models according to the pre-obtained photovoltaic power generation power, the air conditioner system load and the change trend of the environment temperature; the environment temperature and the electric energy are input into the target cold storage capacity calculation model, and the cold storage capacity, output by the target cold storage capacity calculation model, of a phase change cold storage material in the air conditioner system is obtained; based on the cold storage capacity, the photovoltaic power generation power and pre-obtained planned electricity consumption probability distribution, analysis processing is conducted, and an energy management strategy of the air conditioning system is obtained; the energy management strategy is used for guiding the air conditioning system to adopt commercial power supply or photovoltaic power supply, whether to utilize the phase change cold storage material for cold storage or not and whether to start a compressor to assist in executing the cold storage process or not. By adopting the method, the power consumption cost can be reduced.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

An adaptive calibration method and system for an electric energy meter, relating to the technical field of electric energy meter calibration. The method comprises: acquiring basic data and additional data of an electric energy meter (S1); performing time series data alignment, and performing period division on an environment data set to generate a time series segmentation result (S2); acquiring device information of the electric energy meter, performing key feature analysis within a period, and establishing an interference weight factor for each feature (S3); building a performance degradation model (S4); acquiring usage environment data of the electric energy meter by means of a monitoring sensor (S5); then generating an adaptive calibration result on the basis of the performance degradation model (S6); and finally performing electric energy meter calibration management (S7). The present invention solves the technical problem in the prior art where the accuracy of electric energy metering is affected due to untimely calibration caused by an inability to monitor performance degradation of an electric energy meter in real time, thereby implementing effective calibration management for the electric energy meter, ensuring the long-term accuracy and reliability of the electric energy meter, and improving the efficiency of calibration work.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention relates to the technical field of hybrid inverters, and discloses a hybrid inverter supporting reactive compensation and a control method thereof, and the hybrid inverter comprises a DC input unit, a three-phase full-bridge inversion unit, a grid-connected switching unit, a signal acquisition unit and a digital control unit. The direct current input unit comprises a photovoltaic interface, a battery interface and a direct current bus and is used for converging photovoltaic and energy storage electric energy; the three-phase full-bridge inversion unit is composed of a power device and an LCL filter and converts direct current into alternating current. The grid-connected switching unit comprises a static switch and an isolation transformer and is used for controlling power grid connection; the signal acquisition unit detects voltage and current signals; and the digital control unit judges a grid-connected mode or an off-grid mode according to the signal, calculates a reactive current reference value and generates a PWM (Pulse Width Modulation) control signal so as to realize reactive compensation control in the grid-connected mode and the off-grid mode. According to the invention, reactive power compensation capability is provided in both grid-connected and off-grid operation modes, and stable operation of the inverter in various working conditions is ensured.

A vehicle includes a chassis including a first frame rail and a second frame rail, a tractive element coupled to the chassis, a drive motor configured to drive the tractive element to propel the vehicle, and an energy storage system. The energy storage system includes a first battery module and a second battery module. The energy storage system is selectively reconfigurable between (a) a first configuration in which the first battery module and the second battery module each provide electrical energy to the drive motor and (b) a second configuration in which the first battery module is removed from the vehicle and the second battery module provides electrical energy to the drive motor.

The invention discloses a control system for lithium battery power management, and relates to the field of lithium battery control, and the system comprises a voltage input module which is used for externally connecting voltage and supplying power to a battery power supply module and a time delay module; the battery power supply module is used for charging the battery; receiving the battery voltage information, and adjusting the voltage output to the battery according to the battery voltage; the battery module is used for a battery to store electric energy; compared with the prior art, the system has the beneficial effects that the output voltage of the battery power supply module is adjusted through the feedback module, the output voltage of the battery power supply module is relatively large at the beginning, and the charging voltage is reduced along with the increase of the battery voltage, so that a quick charging effect is constructed; whether battery charging is abnormal or not is detected through the time delay module and the charging fault detection module, the battery power supply module is adjusted to perform constant-voltage charging when charging is abnormal, and the battery charging effect is guaranteed.

Various electric energy supply vehicles and electric energy supply vehicle control system; a to-be-replaced electric vehicle and an electric vehicle battery replacement control system; a robot, a robot driving system, a carrying robot and a carrying robot control system; a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of charging and replacing cabinets, a charging and replacing cabinet control system, a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of to-be-replaced electric vehicles with different structures, a constructed electric vehicle finding mode, a plurality of electric vehicles to be subjected to battery replacement, a construction battery swapping station link is omitted, one electric energy supply vehicle can provide replenishment for a plurality of electric vehicles, an electric energy supply vehicle is put according to the amount of electric vehicles, invalid investment is avoided, and a vehicle owner does not need to center the battery again.

The invention relates to the field of load monitoring, in particular to an electric energy meter time-sharing load monitoring method and system based on multi-dimensional electricity utilization characteristics, and the method comprises the steps: collecting and preprocessing the electrical time sequence data of a user electric energy meter; extracting multi-dimensional statistical characteristics of current and voltage of the users under a plurality of analysis windows, and clustering to obtain a cluster and membership degree of each user; for the target user, respectively calculating a first anomaly degree of the target user and a historical sequence thereof and a second anomaly degree of the target user and a sequence of other users in the same period in the cluster to which the target user belongs; according to the membership degree of the user under each analysis window, carrying out weighted fusion on the second anomaly degree to obtain a third anomaly degree; and calculating a comprehensive abnormality based on the first abnormality and the third abnormality, and determining whether the load is abnormal. According to the invention, through dual reference of individual history and a dynamic group and multi-scale credible fusion, the accuracy, the adaptive ability and the reliability of load anomaly monitoring are significantly improved.

The invention provides a shaft power generation and energy storage hybrid power efficiency optimization system for a container ship under multiple working conditions, which is applied to the field of ship energy management, and comprises a working condition strategy module, a fluctuation suppression module and a power distribution module, the working condition strategy module is electrically connected with ship radar navigation equipment and a ship identification system receiver, and the fluctuation suppression module is electrically connected with the ship identification system receiver. The power distribution module is electrically connected with a ship power grid load detector, a main engine rotating speed sensor and an energy storage charge state sensor; according to the invention, by cooperatively regulating and controlling the output power of the main engine shaft power generation equipment, the auxiliary power generation equipment and the composite energy storage equipment, a high-efficiency power calling mechanism for a ship power grid is constructed, so that under the complex ship working condition information, the safety redundancy and reliability of power supply are improved, the utilization efficiency of electric energy is improved, and the energy consumption is reduced. And the fuel consumption is reduced, so that the comprehensive target of energy conservation and emission reduction is achieved.

The invention discloses an accurate zero crossing point control method for a built-in relay of a large-current electric energy meter, which relates to the technical field of power electronics and intelligent metering and comprises the following steps of: receiving a power grid voltage digital signal, determining a voltage zero crossing point by comparing symbol changes of adjacent sampling values, establishing a phase corresponding relation between voltage and current, and calculating a current phase corresponding relation; generating current zero crossing point prediction data; when a relay switching instruction is received, the specific moment of the next current zero crossing point is calculated, and the control signal sending moment is determined; a relay driving control signal is sent, the actual action completion moment of the relay is collected, the deviation from the expected zero crossing moment is calculated, and the mechanical action delay time parameter of the relay is corrected. By predicting the current zero-crossing time and considering the mechanical action delay characteristic of the relay, accurate switching of the built-in relay at the actual current zero-crossing point is achieved, meanwhile, the self-adaptive adjusting capacity is achieved, the arc energy during large-current switching is reduced, the service life of the relay is prolonged, and the reliability of the electric energy meter is improved.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention discloses an electricity meter terminal temperature anomaly identification method, system and device and a storage medium, and relates to the technical field of intelligent electricity meter monitoring and anomaly detection.The method comprises the steps that currents of a live line loop and a zero line loop of an intelligent electricity meter are sampled, and the deviation rate of the live line current relative to the zero line current is calculated; estimating the temperature of the terminal of the electric energy meter based on the deviation rate and by using the resistance characteristic metering error change trend of the manganese-copper shunt; and when the terminal temperature estimated value of the electric energy meter exceeds a preset threshold value, determining that the temperature of the live wire terminal is abnormal, and recording an alarm event. The method realizes real-time acquisition of current difference characteristics, provides a reliable data basis for subsequent terminal temperature estimation, realizes a method of indirectly reflecting terminal temperature rise through electrical characteristics, ensures that alarm can be triggered and a complete tracing chain can be formed when temperature abnormity occurs, and improves the accuracy of terminal temperature estimation. Therefore, the controllability of the operation safety of the electric energy meter and the completeness of post analysis are improved.

The invention relates to the technical field of pump station energy-saving optimization, in particular to a pump station multi-target energy-saving optimization control method and system for self-calibration of a pump efficiency curve, and the method comprises the following steps: arranging a flowmeter, a pressure sensor and an electric energy metering device at key operation parts of a pump station, and collecting the flow, lift and power operation data of a pump set in real time; calculating pump efficiency characteristic points based on flow, lift and power data collected in real time; the characteristic points are compared with a designed rated pump efficiency curve, and if the deviation of the corresponding efficiency value exceeds a preset threshold value, the pump efficiency curve is corrected through a parameter fitting method so as to obtain a corrected pump efficiency curve reflecting the current working condition. According to the method, the flow, lift and power operation data of the pump set are collected in real time, the pump efficiency characteristic points are calculated and compared with the designed rated pump efficiency curve, the pump efficiency curve is dynamically corrected through a parameter fitting method, and the consistency of the pump efficiency model and the actual working condition is achieved.

The invention provides a resource collaborative scheduling system and method for a virtual power plant. The method comprises the following steps: determining space-time probability distribution of wind and light output in the virtual power plant through historical meteorological data and historical illumination data; determining space-time load distribution of an electric vehicle cluster in the virtual power plant, and constructing a source-load interaction scene set under multiple space-time scales in the virtual power plant by fusing the space-time probability distribution and the space-time load distribution; determining a multi-objective optimization function of the virtual power plant according to the price demand signal of the electric energy service market and the source-load interaction scene set; and performing optimization solution on the multi-objective optimization function to obtain a collaborative scheduling plan of the virtual power plant, decomposing the collaborative scheduling plan into a control instruction sequence, and issuing the control instruction sequence to a local controller of each distributed resource. According to the scheme of the invention, a multi-target optimization system considering the operation benefit and the renewable energy power abandonment rate can be constructed through the source-load interaction scene under multiple spatial-temporal scales, so that the closed-loop management and control of the resource scheduling of the virtual power plant can be realized.

A vehicle having a primary mover, a charging system, multiple electric loads, and a controller. The charging system includes a charge storing device and an alternator. The alternator is configured to convert mechanical energy generated by the prime mover into electrical energy to charge the charge storing device. The multiple electrical loads are electrically coupled to the charging system via a power distribution module. The controller is communicably coupled to the charging system and is configured to receive an indication that an electrical output of the charging system is unable to provide sufficient electrical energy to each electrical load in the electrical loads. The controller is also configured to provide a control signal to the power distribution module in response to the indication. The control signal is configured to cause the power distribution module to decouple at least one of the electrical loads from the charging system.

The invention relates to the technical field of micro-grid control, and discloses a micro-grid adaptive switching control method and system based on multi-source information fusion, and the method comprises the steps: obtaining the internal operation parameters of a micro-grid, and the state parameters of an external large power grid connected with the micro-grid; the internal operation parameters and the external large power grid state parameters are fused, and a real-time comprehensive operation state model of the micro-grid is constructed; based on the real-time comprehensive operation state model, adaptively determining a target operation mode of the micro-grid; and according to the target operation mode, generating and executing a smooth switching control instruction of the micro-grid. According to the method, the multi-source operation information of the large power grid inside and outside the microgrid is fused, the comprehensive operation state model is constructed in real time, the target operation mode is adaptively selected, and switching control is smoothly executed, so that efficient and stable switching of the microgrid between multiple modes such as grid connection and island can be realized, and the power supply reliability, the system stability and the electric energy quality are improved.

The invention discloses a cooperative scheduling method for a zero-carbon park complementary energy storage system, and the method comprises the steps: enabling an electric energy quality index to be explicitly incorporated into an optimization target through multi-source resource dynamic modeling and scene prediction, and building a strong coupling relation between a physical constraint and a scheduling decision; the hierarchical execution mechanism gives consideration to global optimization and local quick response, realizes undisturbed switching under abnormal working conditions, forms a prediction-optimization-execution-feedback closed-loop control system, and can accurately describe physical connection and electrical characteristics of a park power grid by establishing a power distribution network equivalent model and acquiring topological parameters, thereby realizing the optimal control of the park power grid. And basic network data is provided for subsequent optimization. By determining the controllable resource set and completing topological mapping, the position and the regulation and control range of each device in the power grid can be determined, and mistaken sending or conflict of instructions can be avoided. An apparent power upper limit constraint and SOC dynamic model is established, overload operation of equipment can be avoided, the energy storage charging and discharging capacity can be accurately represented, and the performability of a scheduling scheme is ensured.

The invention relates to the technical field of electric energy quality monitoring and treatment, and discloses an electric energy quality abnormity traceability and purification strategy generation system, which comprises an electric energy acquisition module, a feature analysis module, an abnormity traceability module, a pollution evaluation module, a parameter optimization module and a strategy generation module. The electric energy acquisition module acquires waveform data of key nodes of a group network in real time and sets a monitoring interval; the feature analysis module decomposes the spectrum features to generate pollution feature vectors; the abnormity traceability module extracts a core index to establish a traceability rule; the pollution evaluation module identifies a propagation path and calculates a pollution index; the parameter optimization module deduces an optimal parameter generation deviation sequence; and the strategy generation module integrates the sequence to form a purification strategy. The system realizes accurate traceability and dynamic management of abnormities, improves analysis comprehensiveness and strategy intelligence level, and is suitable for power grid electric energy quality management and control.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

The invention discloses a pressure storage and heat storage coupled regenerative heat pump power storage system and a regulation and control method thereof. The system is composed of an electric-power generation integrated unit, an energy storage compression and energy release expansion integrated unit, an energy storage expansion and energy release compression integrated unit, a high-temperature heat exchanger, a backheating heat exchanger, a cold energy absorption heat exchanger, a heat storage and cold storage storage tank, a high-pressure gas storage tank and the like. And an energy storage / energy release loop, a heat storage / heat release loop, a cold storage / cold release loop and a pressure heat coupling loop are formed by connecting pipelines. In the energy storage mode, the compressor unit is driven by electric energy to conduct multi-stage compression, and compression heat and expansion cold are stored in the high-temperature storage tank and the low-temperature storage tank correspondingly. In the energy release mode, the heat storage medium and the cold storage medium are sequentially released to supply heat or cold to the circulating working medium, and the expansion unit is driven to drive the generator to generate electricity. According to the method, bidirectional operation of energy storage and energy release is achieved through valve switching and circulation control, and power output and load change are adjusted within a second level through a pressure-heat coupling mechanism.

The invention relates to an anti-magnetic interference intelligent electric energy metering box, and relates to the technical field of electric energy metering boxes, the anti-magnetic interference intelligent electric energy metering box comprises a meter box main body and a shielding assembly, the shielding assembly comprises a shielding box for installing the meter box main body, and a shielding cover detachably installed at the front side of the shielding box; heat dissipation openings are formed in the middle parts of the left and right vertical side surfaces of the shielding box, wiring openings are formed in the lower parts of the left and right vertical side surfaces of the shielding box, and shielding nets are fixedly arranged on the inner walls of the heat dissipation openings; protection plates are rotationally connected to the edges, close to the back side, of the left and right side surfaces of the shielding box, and a plurality of fixing holes are formed in the protection plates; limiting plates are fixedly arranged on the top faces of the two protection plates in the horizontal direction, and a locking piece is arranged on the top face of the shielding box. During carrying, the protection plates are rotated to be in a protection state of being attached to the left side face and the right side face of the shielding box, at the moment, the protection plates can play a role in protecting the shielding nets at the heat dissipation openings, and the shielding performance of the electric meter box is guaranteed.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention relates to the field of data processing, in particular to an electric energy meter abnormity monitoring method and system based on digital twinning, and the method comprises the steps: obtaining a historical data time sequence of an electric energy meter, and carrying out the preprocessing; dividing the historical data time sequence of each dimension to obtain a plurality of historical data sets containing historical data time sequence segments of each dimension, and training an LSTM model based on the historical data sets; acquiring a current data time sequence of the electric energy meter and preprocessing the current data time sequence to obtain a current data set, inputting the current data set into the trained LSTM model, outputting predicted values of a plurality of target dimensions at the next moment, obtaining measured values of the plurality of target dimensions at the next moment, calculating a judgment index based on the predicted values, the measured values and a historical data set, and determining the current data time sequence of the electric energy meter. And judging the abnormal condition of the electric energy meter based on the judgment index. According to the method, noise residual errors generated when the self-cognition uncertainty of the model is high can be automatically inhibited, meanwhile, a real state deviation signal during cognition determination is amplified, and the accuracy of anomaly judgment is improved.

The invention belongs to the technical field of intelligent electric meters, and discloses an intelligent electric meter system with an abnormal power consumption behavior recognition function. The system is composed of a data acquisition module, an electric energy quality monitoring module, a data preprocessing module, a power utilization behavior feature extraction module, a short-time behavior monitoring module, an anomaly detection and diagnosis module, a behavior trend analysis module, an alarm and visualization module and a remote collaborative management module. Through deep mining and learning of historical power utilization data of a user, the system can accurately grasp power utilization habits, time period change rules and load characteristics of the user, along with dynamic evolution of power utilization conditions, the system can automatically optimize an anomaly detection threshold value, limitation brought by a traditional fixed threshold value is abandoned, and through the personalized and adaptive design, the power utilization efficiency of the user is improved. The system can exert the optimal efficiency in different regions and different types of users, and the application range and the practicability of the system are remarkably improved.

The invention discloses a method for detecting abnormal metering performance of an intelligent electric energy meter, which belongs to the technical field of electric energy metering equipment and comprises the following steps of: 1, establishing a reference response curved surface of parasitic parameters of a voltage sampling resistor and a current transformer in the electric energy meter relative to temperature and humidity; step 2, during the operation period of the electric energy meter, acquiring the real-time measurement value of the parasitic parameter in situ; and step 3, matching the real-time measurement value with the reference response curved surface. According to the method, an active matching model library can be constructed by fusing an underlying physical model of a component and a mapping rule set for a complex environment with sudden change of plateau outdoor temperature and humidity, the internal mechanism that parasitic parameters change along with temperature and humidity is explained from the physical essence level, and the limitation of a pure data statistical method is made up; the dynamic error judgment threshold value can adapt to environmental stress changes in real time, and the accuracy of metering performance anomaly detection and the complex environment adaptability are effectively improved.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.