99 results about "Electrical model" patented technology

The invention discloses a power distribution system fault analysis method and system. The method comprises the steps of collecting operation related data of a power distribution system in real time through various sensors and intelligent metering equipment; preprocessing the collected operation related data to remove noise and interference signals, and establishing a multi-dimensional feature set of the real-time operation state of the power distribution system; extracting potential fault features from the multi-dimensional feature set by using a convolutional neural network, and generating a fault feature vector; performing time sequence analysis on the fault feature vector by using a recurrent neural network, and identifying a possible fault type in the current power distribution system; and in combination with the topological structure and the electrical model of the power distribution system, spatial positioning is performed on the identified fault type based on the graph neural network, and specific nodes of the fault and corresponding fault severity levels are output. According to the embodiment of the invention, the fault detection capability and response speed of the power distribution system can be remarkably improved, and the power distribution system is promoted to develop towards intelligentization and automation directions.

The invention discloses an electric vehicle charging monitoring method and device based on a heterogeneous feature fusion network, and the method comprises the steps: S01, collecting various time domain feature parameters and high-frequency key current signals of an electric vehicle battery in different charging states, and building a sample database; step S02, constructing an electric vehicle charging state prediction model, wherein the model comprises a time-frequency feature fusion network, a CNN-LSTM feature fusion module and a predicted value output module which are connected in sequence; step S04, the edge device receives the time domain characteristic parameters and the high-frequency key current of the tested electric vehicle battery in real time, and calls the deployed electric model for prediction to obtain a charging state prediction value; and S05, judging the abnormal charging state of the detected electric vehicle according to the state prediction value. The method has the advantages of being simple in implementation method, low in cost, high in monitoring precision, fast in early warning response, easy to apply on a large scale and the like.

The invention relates to the technical field of relay protection, in particular to an automatic checking method and system for a relay protection setting value of a power plant, and the method comprises the steps: obtaining relay protection data, including obtaining electrical parameters, equipment parameters and power topological structure information; constructing a topological graph model of the power network by using the power topological structure information, constructing an electrical model based on elements according to the electrical parameters and the equipment parameters, and combining the topological graph model with the electrical model by using node parameter association to obtain a power system integrated model; and performing constant value check calculation based on the power system integrated model, and generating a report according to a constant value check calculation result. Hidden layers are set for different fault types by adopting the multi-branch structure network, so that the neural network can perform specialized learning for different types of fault features, and the adaptability of the system to different fault scenes is enhanced.

The invention relates to the technical field of computer simulation, in particular to a thermal simulation method and system for a semiconductor power assembly. The method comprises the following steps: obtaining geometric structures, material attributes and working characteristics corresponding to each semiconductor power assembly, and carrying out finite element simulation modeling to generate a finite element model of each semiconductor power assembly; performing heat source distribution response analysis on the finite element model of each semiconductor power component based on the working characteristics to generate corresponding heat source distribution fields of each semiconductor power component under different working conditions; based on material attributes, heat conduction simulation analysis is carried out on heat source distribution fields corresponding to the semiconductor power assemblies under different working conditions, and a semiconductor assembly electrical model is constructed to carry out electric heat distribution influence evaluation calculation and thermal simulation iterative optimization; and generating a heat flow temperature distribution simulation optimization result among the semiconductor power components. The thermal simulation precision corresponding to the semiconductor power assembly can be improved.

The invention discloses a dual-motor driving system of a variable-stiffness robot joint and a dual-motor cooperative variable-stiffness control method of the dual-motor driving system. The dual-motor cooperative variable-stiffness control method comprises the steps that motor parameters of a first motor and a second motor and joint parameters of the variable-stiffness robot joint are obtained; constructing a motor electrical model, a motor mechanical model, a joint mechanical model and a spring-damping model of the backlash according to the motor parameters and the joint parameters; determining a total optimization objective function, and solving the total optimization objective function according to the motor electrical model, the motor mechanical model, the joint mechanical model and the spring-damping model to obtain a first current error and a second current error; designing a cooperative controller according to the first current error and the second current error, and outputting a control quantity; and performing dual-motor cooperative variable stiffness control on the first motor and the second motor according to the control quantity. The response speed and rigidity flexibility of the variable-rigidity robot joint can be improved, and the variable-rigidity robot joint can be widely applied to the technical field of motor control.

The invention belongs to the technical field of lithium ion batteries, and discloses a novel lithium ion battery estimation method and system considering the core temperature, and the method comprises the steps: building a thermoelectric coupling model of a lithium ion battery, the thermoelectric coupling model comprises an electric model and a thermal model, respectively determining parameters of the electric model and the thermal model by adopting a maximum likelihood method MLM and an adaptive genetic algorithm AGA; on the basis of the thermoelectric coupling model with the determined parameters, an unscented Kalman filter UKF is adopted to estimate the SOC, the terminal voltage and the battery nuclear temperature of the lithium ion battery, a basis is provided for energy management and service life prolonging of the lithium battery, and the safety of the lithium battery at the high temperature can be improved.

The invention relates to the technical field of motor control, and discloses a sensorless direct-current brushless motor control system which comprises a motor modeling module, a state estimation module, a deep learning prediction module, a filtering fusion module, a self-adaptive control module and a control execution module. The invention also discloses a sensorless direct current brushless motor control method, which comprises the following steps of: model establishment: constructing an electrical model and a mechanical model for describing the dynamic behavior of the motor so as to obtain a motor operation dynamic model suitable for control calculation; and state estimation: performing preliminary estimation on the rotor position and the rotor speed of the motor by utilizing the running dynamic model and combining current and voltage real-time signals acquired in the running process of the motor. According to the method, high-precision and high-stability control of the motor under different load and operation conditions is realized through adaptive control and rotor state estimation optimization technologies, real-time dynamic adjustment and commutation accuracy of the motor are ensured, and the overall control performance is improved.

The invention discloses a resistance-type high-temperature superconducting current limiter simulation model building method, which is used for carrying out analogue simulation on a resistance-type superconducting current limiter made of YBCO (Yttrium Barium Copper Oxide) superconducting tape, and comprises the following steps: S1, building an electrical model module of the resistance-type superconducting current limiter; s2, building a thermodynamic model module of the resistance type superconducting current limiter; s3, carrying out iterative circulation on the electrical model module and the thermodynamic model module; and S4, packaging the electrical model module, the thermodynamic model module and the oscilloscope reading module to obtain a resistance-type superconducting current limiter simulation model so as to access a power grid model. According to the invention, analogue simulation of the resistance-type high-temperature superconducting current limiter can be realized, and the resistance-type high-temperature superconducting current limiter can be connected into a simulation power grid for testing.

The invention discloses a battery temperature estimation method and an electric bicycle, and relates to the technical field of battery management. The method comprises the following steps: constructing an electrical model and a thermal model of a battery under a preset operation condition; constructing an electrothermal coupling model of the battery under a preset operation condition according to the electrical model and the thermal model; the electrothermal coupling model is used for describing the electrical and heat change process in the battery; and based on the electrothermal coupling model and the electrical measurement data of the battery in the charging and discharging process, estimating the internal temperature and the surface temperature of the battery under the preset operation condition by adopting an extended Kalman filtering algorithm. Compared with a traditional monitoring method depending on a temperature sensor, the internal and surface temperature changes of the electric bicycle battery can be reflected more accurately, thermal characteristic changes under complex working conditions can be dynamically adapted, the complexity and the cost of the system are reduced, and the safety and the reliability of the battery management system are effectively improved.

The invention belongs to the technical field of geophysical exploration, and particularly discloses a ground well controllable source electromagnetic method joint inversion method and system based on a tensor source, and the method comprises the steps: carrying out the forward modeling of an established three-dimensional electrical model through a CSEM in a ground well, and obtaining the impedance tensor and a tipper of a tensor controllable source; constructing a three-dimensional ground and in-well CSEM combined inversion objective function and a gradient of the inversion objective function by using the model roughness matrix and the data fitting item; and solving the gradient of the inversion objective function by adopting an improved L-BFGS optimization algorithm based on a momentum method, and inverting the underground electrical structure. According to the method, through joint inversion and in combination with controllable source electromagnetic data in the ground and the well, the transverse and longitudinal resolution of exploration is improved, the multiplicity of inversion is reduced, and the inversion result is more reliable.

The embodiment of the invention provides a cable early-stage fault positioning method and device based on metal sheath parameters. The cable early-stage fault positioning method based on the metal sheath parameters comprises the steps that a cable electrical model is established based on electrical parameters; obtaining a key electrical quantity, comparing the key electrical quantity with a preset threshold value, and determining a comparison result; determining fault feature information based on the comparison result; and determining a fault point based on the fault feature information and the fault positioning equation. According to the scheme, the impedance characteristic of the cable metal sheath is utilized, and the parameters of the metal sheath are introduced into a fault positioning algorithm. According to the method, the positioning error is effectively reduced, the accuracy of the early fault positioning process is remarkably improved, and a more efficient and more accurate solution is provided for solving the detection and positioning problems of the power system in the early stage of the fault.

The invention provides a virtual power plant efficient aggregation intelligent scheduling method and system oriented to wide-area distributed resources, and the method comprises the steps: selecting a plurality of distributed resources, depicting a feasible region of each resource, and constructing a dynamic electrical model corresponding to each resource; the method comprises the following steps of: approximating feasible regions of various distributed resources based on Zonotope so as to aggregate the distributed resources; converting the aggregated Zonope form into a half-space form to obtain a first constraint condition for optimization regulation and control, and constructing an aggregation resource cluster optimization regulation and control model by taking optimal operation efficiency of the virtual power plant VPP as a target; and solving the optimization regulation and control model under a first constraint condition, and performing two-stage regulation and control on each distributed resource cluster according to a solving result. According to the method, the accuracy degree of aggregation regulation and control of the distributed resources in the virtual power plant scene is improved, the flexibility of the distributed resources is ensured, and optimal configuration of the resources is realized.

The invention discloses a distributed flexible resource energy management method and system for a virtual power plant. The method comprises the steps of designing a distributed flexible resource energy management system framework in a virtual power plant scene, managing two distributed flexible resources of an energy storage system and a water treatment system by controlling charging and discharging power of a battery and operating power of a water pump, and constructing dynamic electrical models of the energy storage system and the water treatment system; establishing a dynamic energy management model of each distributed flexible resource based on a Markov decision process by taking economical efficiency maximization as a target and considering power flow constraints of the power distribution network; and training the dynamic energy management model through an improved deep reinforcement learning algorithm to form a real-time energy management strategy of the distributed flexible resources. According to the scheme of the invention, distributed flexible resource operation regulation and control considering high efficiency, economical efficiency, robustness and safety is formed.

The invention relates to a power distribution network planning adjustment method and device based on digital twin and multi-objective optimization, belongs to the technical field of power distribution network planning, and solves the problems that an existing planning result is not accurate enough, the anti-risk capability is insufficient and a dynamic adjustment mechanism is lacked. The method comprises the following steps: constructing a digital twinborn model of the power distribution network; a prediction model is constructed, and a prediction result is fed back to the digital twinborn model; introducing Monte Carlo simulation to generate a plurality of operation scene sets, executing digital twin simulation on each operation scene set to obtain network state distribution under different scenes, and quantifying system response characteristics under uncertainty; constructing a multi-target robust optimization model in combination with the network state distribution and the system response characteristics, and optimizing a power distribution network planning and equipment investment scheme; and the actual power distribution network investment data and the operation state are transmitted back to the digital twinborn model so as to adjust the electrical model and the prediction model of the digital twinborn model. And closed-loop self-learning of planning-implementation-correction is realized.

A method for monitoring a battery system is proposed. The method includes providing a sequence of pairs of measured current and voltage values that follow one another in time. The pairs of current and voltage values indicate the current flowing through the battery system and the voltage present at the battery system. The method also provides for an electrical equivalent model of the battery system to be provided. The equivalent electrical model has several impedances connected in series. Initial impedance parameter values are provided for the impedances of the equivalent electrical model. The method provides for adjusting a first impedance parameter of an impedance of the equivalent electrical model based on the difference between a first voltage value simulated based on the initial impedance parameter values and the first current value of the finite sequence and the first measured voltage value of the finite sequence. An optimized first impedance parameter value is thereby obtained. By minimizing the deviation of a sequence of simulated voltage values obtained on the basis of the electrical equivalent model and the first sequence of measured current values from the sequence of measured voltage values by adapting at least one further impedance parameter of the impedances of the electrical equivalent model, at least one optimized further impedance parameter value of the at least one further impedance parameter is obtained. A battery management system for carrying out the method is also proposed.

The invention provides a deep and large fracture depicting method based on low-frequency electromagnetic information recovery enhancement, and the method comprises the steps: S1, collecting electromagnetic data, and carrying out the preprocessing of the electromagnetic data, and obtaining the preprocessed electromagnetic data; s2, obtaining frequency domain apparent resistivity and phase according to the preprocessed electromagnetic data; s3, establishing a shallow stratum electrical model; s4, acquiring a depth domain apparent resistivity profile according to the shallow stratum electrical model; and S5, depicting deep and large fracture distribution based on the depth domain apparent resistivity profile. According to the invention, enhancement of low-frequency electromagnetic signals is realized, the inversion precision of electromagnetic data is improved, low-frequency geological information is effectively recovered, and deep and large fractures can be effectively described.

The application discloses a kind of electrical model light weight translation software methods based on IFC data format, it is related to model data processing technical field, including the following steps: extracting industrial foundation IFC data format electrical model geometry data, attribute data and topological relation data, based on the electrical equipment shell of the electrical model, electrical circuit and electrical component, the electrical model is divided into geometry layer, attribute layer and topological layer, and the geometry layer, attribute layer and topological layer are removed to redundant information, obtain the simplified simplified electrical model.This is through the geometry, attribute and topological data of electrical model and implements hierarchical light weight processing, rather than using general simplification strategy, effectively removes the redundant information of each level, achieves the effect of accurately reducing model data amount, realizes the efficient transmission and quick loading of translated model in different hardware terminal, improves the basic application ability of model cross-device platform.

An optimization method for BLDC non-inductive low-speed starting based on IF starting and flux estimation, comprising the following steps: step one: establishing a BLDC motor electrical model, and obtaining relevant parameters for building a current double closed loop model through the BLDC motor electrical model; step two: based on the simulink simulation software, building a current double closed loop model of d and q axes; step three: based on the current double closed loop model built in step two, realizing IF open loop low-speed starting and acceleration of the BLDC motor; step four: constructing a discrete nonlinear flux observer, and online estimating the flux to obtain the rotor electric angle_ref and the equivalent back electromotive force component; step five: according to the equivalent back electromotive force component, online following and correcting the open loop electric angle. The present application can realize quick and undisturbed switching from open loop starting to non-inductive closed loop control, thereby significantly improving the starting success rate and operation stability of the BLDC motor under low-speed working conditions.

The present invention relates to a sensing system (10) for monitoring a substrate (1), comprising a sensing unit (4) with first and second sensing elements (3A, 3B) configured to be inserted into the substrate (1); a signal generation unit (5) configured to apply an electrical signal to the sensing elements; a readout unit (6) configured to determine at least one electrical parameter as a result of the electrical signal being applied to the sensing elements (3A, 3B); and a processing unit (7) configured to determine at least an amount of water inside the substrate (1) among an amount of water in the substrate (1) and a nutrient concentration in the substrate (1), based on the determined at least one electrical parameter and an electrical model of a combination of the sensing elements (3A, 3B) and the substrate (1) in which they are inserted, wherein the electrical model comprises a first inductor modelling a first inductance (Lp1) of the first sensing element (3A) and a second inductor modelling a second inductance (Lp2) of the second sensing element (3B); a capacitor modelling a capacitance (Cp) between the sensing elements (3A, 3B); a resistor modelling a conductance (Gp) between the sensing elements (3A, 3B); wherein the first inductance (Lp 1) is modelled as a known first function of the amount of water inside the substrate (1), wherein the second inductance (Lp2) is modelled as a known second function of the amount of water inside the substrate (1), wherein the capacitance (Cp) is modelled as a known third function of the amount of water inside the substrate (1), and wherein the conductance (Gp) is modelled as a known fourth function of the amount of water inside the substrate (1) and an amount of mobile ions representing the amount of nutrients inside the substrate (1).

The invention provides a construction method of a voltage segmentation and layering calculation framework. The construction method comprises the following steps: summarizing node voltage historical operation data and node active power historical operation data in a low-voltage power distribution network, and determining a daytime operation time period and a night operation time period; obtaining a daytime training data set according to the daytime operation time period, and obtaining a night training data set according to the night operation time period; according to the daytime training data set and the night training data set, constructing a base layer and a regulation and control layer of the low-voltage power distribution network in different operation periods; and acquiring basic voltages and voltage variations of the low-voltage distribution network in different time periods, and calculating actual voltages of the low-voltage distribution network in different time periods according to the basic voltages and the voltage variations. According to the technical scheme of the embodiment, the non-electrical model voltage calculation accuracy of the low-voltage active power distribution network can be improved.

The application provides a SiC MOSFET behavior modeling method based on physical mechanism optimization, comprising: for a SiC MOSFET output characteristic curve, constructing a static electrical model structure of the SiC MOSFET on the basis of considering a drain-induced barrier lowering effect of the SiC MOSFET; based on the static electrical model structure, adopting a voltage function to describe nonlinear behavior of an interelectrode capacitance varying with a bias voltage, and constructing a dynamic characteristic model structure of the SiC MOSFET; adopting a method combining experimental data and structure function analysis to perform physical mechanism optimization on the static electrical model structure and the dynamic characteristic model structure, and obtaining a final required SiC MOSFET behavior model. The application can accurately reflect static and dynamic characteristics of the SiC MOSFET device, improve precision, applicability and convenience of parameter extraction of the model, and meet design requirements of a new generation of high-performance power electronic systems.

This invention discloses a method for analyzing the high-temperature gate bias performance variation of p-gate GaN HEMT devices, comprising the following steps: First, parameters are acquired and two-dimensional modeling is performed on the p-gate GaN HEMT device, and a fine mesh is generated. Next, an electrical model is constructed and optimized. After verifying the consistency between the model and the device datasheet, the operating conditions of the device are set, including temperature and bias voltage. Simultaneously, models such as lattice heating, traps and interface states, and mobility are introduced to accurately simulate the device's performance under actual operating conditions. Subsequently, the influence of different HTGB stress times on the internal defect density, charge concentration, and carrier concentration of the device is analyzed by introducing a time model. Finally, the electrical characteristics of the device after different HTGB stress times are simulated and analyzed to evaluate the device's reliability. This invention intuitively demonstrates the impact mechanism of HTGB on device performance degradation and can assess the trend of device performance changes over time.

The present invention relates to a method, device and medium for regulating the electrolytic lead load to participate in the power grid demand response, belonging to the technical field of electrolytic lead load regulation. The method includes the following steps: obtaining the operation data of the electrolytic lead load electrolytic cell to determine the equivalent resistance of the electrolytic cell, and constructing an electrical model based on the equivalent resistance and the operation data to output the power of the electrolytic cell. Calculating the power adjustment amount based on the power command value issued by the power grid and the power of the electrolytic cell. Constructing an optimization model with the goal of minimizing the power adjustment amount and formulating corresponding constraint conditions. At the same time, constructing a power adjustment amount change model. Under the condition of meeting the constraint conditions, solving the optimization model to obtain the current adjustment amount, and adjusting the current passing through the electrolytic cell accordingly. By collecting the operation data in real time, dynamically calculating the equivalent resistance and constructing an electrical model, the present invention can dynamically reflect the actual operation state of the electrolytic cell, avoiding the problem of the decrease in the model accuracy caused by fixed or untimely updated parameters.

FIELD: medicine.SUBSTANCE: group of inventions relates to a method for multi-frequency bioelectrical impedance analysis and a device for implementing it. Alternating electric current signals are passed through the human body. Each signal has one of three different frequencies. At each frequency, the amplitude of the voltage of the signals passing through the human body is measured. An initial phase angle is selected for each specific impedance module. The phase angle characterizes the phase shift of the current of signals entering the human body relative to the phase of the voltage of signals passing through the human body. At each frequency, the phase angle is estimated using an equivalent electrical model of the human body, the initial phase angle and the impedance modulus. A body model is created for a specific person. The ratio of extracellular water content to total body water content in a human is determined by inputting extracted parameters of the human body model into a trained machine learning model. The device comprises a source of alternating electric current signals, electrodes for conducting and reading signals, a measuring device for measuring signal parameters, a processor and memory.EFFECT: device is compact and has small-area electrodes for integration into wearable and / or portable devices while maintaining measurement accuracy.14 cl, 6 dwg

The invention discloses a power distribution network node self-triggering power control method, a control device and a planning device. The method comprises the following steps: acquiring a topological structure of a power distribution network and node information data of each node in the topological structure; according to the topological structure and the node information data of each node in the topological structure, respectively constructing a primary side electrical model and a secondary side information model of the power distribution network; on the basis of the primary side electrical model and the secondary side information model, a combined state vector is generated through a coupling function, and a primary and secondary fusion model of the power distribution network is given in combination with constraint conditions; based on a service interaction mode of digital twin rehearsal, combined with a primary and secondary fusion model of the power distribution network, providing a node for triggering control in the power distribution network and a corresponding power control function so as to carry out node self-triggering power control of the power distribution network. According to the method, the non-perception power supply capability of the load can be ensured, and finally, the reliability, the expandability and the engineering applicability of the power distribution network in a non-power-cut operation scene are remarkably improved.

The invention discloses a BIM-based cable intelligent optimization system and method, the system is composed of an electrical system diagram module, a fast connection model module and a cable laying module, the electrical system diagram module can perform data interaction with an electrical BIM model, cable model basic data is imported, and the fast connection model module is connected with the cable laying module; the method comprises the following steps: identifying and reading imported electrical design drawing information, and analyzing identified electrical information data to identify abnormal data information; the quick connection model module can quickly communicate the component system based on a one-to-one connection rule in the electromechanical model; and the cable laying module can simulate the actual arrangement scene of the cables of each power distribution system in the electrical BIM model, perform comparison and analysis of various schemes according to different design requirements and laying scenes, and generate an optimal cable arrangement scheme. According to the scheme, automatic examination of an electrical system diagram, intelligent planning of a cable path, accurate calculation of a bridge filling rate, automatic export of a cable quantity list and optimization of cable arrangement are realized, and the overall quality and efficiency of building electrical construction are improved.

The invention provides a voltage fitting method without an electrical model. The method comprises the steps that an active power matrix and a reactive power matrix of a low-voltage power distribution network are acquired, and an input data set is constructed; inputting the input data set into a width learning model to obtain an output voltage of the low-voltage power distribution network and taking the output voltage as an output data set of the width learning model; acquiring an original input data set of the low-voltage power distribution network, performing dynamic principal component analysis on the original input data set to obtain a dynamic latent variable data set, and inputting the dynamic latent variable data set instead of the original input data set into the width learning model for training; extracting a dynamic feature weight in the dynamic latent variable data set, and replacing a random weight of the width learning model with the dynamic feature weight to obtain a width learning voltage fitting model; and performing parameter optimization according to a preset particle swarm algorithm to obtain an optimal parameter of the voltage fitting model. According to the technical scheme of the embodiment, the precision of voltage fitting in the low-voltage power distribution network can be remarkably improved.

The invention belongs to the technical field of power distribution networks, particularly relates to a power distribution photovoltaic access capacity evaluation method of a lightweight four-fusion device, and aims at solving the problems that an existing evaluation method depends on a detailed power grid model, calculation is complex or precision is insufficient. The method comprises the following steps: acquiring local electrical quantity time sequence data, constructing an equivalent electrical model and determining an equivalent impedance parameter; performing iterative simulation based on the equivalent electrical model, and gradually increasing the accumulated virtual photovoltaic grid-connected capacity by superposing a preset power value serving as an iteration step length in each iteration process; and when the key electrical quantity of the monitoring point reaches a preset operation constraint boundary, stopping the iteration process, and determining the accumulated virtual photovoltaic grid-connected total capacity before the last iteration process causing iteration stopping as the photovoltaic accessible capacity of the monitoring point. According to the invention, the method does not need to obtain the topological parameters of the whole network, only depends on the local measurement data, and achieves the efficient and precise evaluation of the photovoltaic access capacity.

The invention relates to the technical field of micro-grid operation control and energy management, and discloses a micro-grid intelligent management and control terminal. An HMI module; an encryption module; the communication module is used for reading topological information of the micro-grid and collecting real-time output and load power requirements of the distributed power supply; and the calculation module is used for constructing a micro-grid electrical model by using the topological information, generating an optimization model taking maximization of local consumption of new energy as a core target based on the micro-grid electrical model, refreshing the optimization model by using real-time output and load power requirements of the distributed power supply, and outputting a control instruction of the micro-grid. According to the invention, the micro-grid modeling and optimization algorithm is integrated to the local terminal, through optimal power flow calculation and multiple safety verification, the optimization regulation and control instruction is generated and issued, the global optimization and closed-loop control of the micro-grid are realized, and the economy, safety and new energy consumption capability of system operation are effectively improved.

The invention discloses an electrical information collaborative management method, belongs to the field of electrical equipment operation and maintenance management, and solves the problems that drawings are inconvenient to draw, updating and intercommunication are difficult, query is split, and accessories are troublesome to compile in the operation and maintenance of existing electrical secondary equipment. The system is adaptive to multiple operating systems based on software, and comprises five basic modules of networking management, model management, system management, drawing information and information query and extension modules of action simulation and the like. The networking management realizes unit authority control and information pushing, the model management establishes a standardized component model containing a three-dimensional / electrical model and associates with a DWG file, the system management constructs a three-layer systematic model, drawing information is rendered in real time, a drawing and a printing format are output, and information query supports multi-condition retrieval and loop visualization. According to the method, drawing management is optimized, cross-unit cooperation efficiency is improved, construction operation and maintenance burden is reduced, simulation verification and iterative optimization are supported, and the method is suitable for dense electrical equipment scenes such as hydraulic power plants.