1701 results about "Energy based" patented technology

The invention relates to the technical field of audio noise reduction, and discloses a Bluetooth headset environment noise adaptive adjustment method and system, and the method comprises the steps: collecting multi-band sound data of headset environment noise through a preset acoustic sensor array, generating a noise spectrogram, extracting a noise masking threshold through time-frequency joint analysis, and collecting a headset audio signal; next, noise suppression is carried out on the audio signal by using a multi-order digital filter bank to obtain a primary noise reduction signal, the primary noise reduction signal is optimized into an optimized noise reduction signal based on a signal-to-noise ratio, and the frequency response matching degree of the optimized noise reduction signal and the original audio signal is calculated; then, performing phase correction and amplitude-frequency equalization processing according to the frequency response matching degree to obtain an audio equalization parameter; and finally, monitoring residual noise energy based on the parameter, generating a noise regulation and control instruction, driving adaptive sampling, and generating an adaptive noise reduction scheme. According to the invention, the audio noise reduction effect of the Bluetooth earphone can be improved.

The invention provides a hydrogen energy storage equipment leakage fault early warning method and system based on deep learning, and the method comprises the steps: collecting the multi-source sensing data of hydrogen energy storage equipment in real time, constructing a multi-dimensional feature matrix, building the nonlinear mapping of pressure fluctuation and gas concentration through a bidirectional interaction network, and carrying out the early warning of the leakage fault of the hydrogen energy storage equipment. And performing spatial modeling on the temperature gradient and the vibration spectrum to generate a fusion feature tensor. And then, inputting the fused feature tensor into a spatio-temporal reasoning model, predicting equipment leakage and energy loss, and outputting a leakage probability cloud picture and an energy loss vector. And an entropy sudden change area is identified based on an energy loss vector, when the high-density leakage area and the entropy sudden change area are overlapped in a continuous detection period, a multi-stage early warning signal is triggered, a leakage source thermodynamic diagram and a pressure balance parameter set are generated, a self-adjusting system is activated to dynamically correct the valve opening degree, network weight is updated, and a closed-loop control link is formed. The early warning accuracy and the response speed of the leakage fault of the hydrogen energy storage equipment are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention discloses a covariance self-adaptive control method for a quadruped robot for spine joint adjustment. The method comprises the following steps that 1, a spine quadruped robot kinematics model with spine joints, a robot trunk dynamics model with the spine joints and a robot leg multi-rigid-body dynamics model are established; 2, based on the energy stability margin theorem and the spine joint coupling effect, a posture control method of the spine joint quadruped robot is provided by combining a covariance self-adaptive evolutionary strategy, and the angle, rigidity and damping coefficient of spine joints are estimated and adjusted according to the terrain gradient; 3, extracting the spatial relationship between the trunk mass center and the foot ends of the robot as an expected reference, updating the expected mass center position in real time, and constructing a model prediction control method to control the posture of the spinal joint quadruped robot; 4, the position of the foot falling point of the swing leg is designed in advance in combination with the additional freedom degree provided by the spine joint and MPC prediction state information. The motion stability of the robot in the complex terrain is improved.

The invention discloses a photovoltaic glass defect detection method and system based on image recognition, particularly relates to the technical field of visual inspection, and is used for solving the problem that defect recognition is interfered by optical artifacts caused by a film layer interference effect on the surface of coated glass. The method comprises the following steps: extracting interference characteristics through a reflection image to generate an optical artifact distribution diagram, positioning an interference region in a transmission image frequency domain space, constructing a process response template in combination with a sedimentary pulse frequency, and executing polarization coherence screening and time-varying deconvolution collaborative filtering to eliminate artifacts; based on energy ratio dynamic decision reinspection or defect classification process implementation, inherent optical noise and real physical defects of a material are effectively separated, and the accuracy and reliability of coated glass defect detection are remarkably improved.

The invention discloses a diastrophic stratum deformation analysis and calculation method for an active fault zone, and relates to the technical field of geological engineering calculation, and the method comprises the following steps: (1) constructing a three-field dynamic coupling model based on an energy density gradient, and unifying dimensions of physical parameters of a mechanical field, a seepage field and a temperature field as an energy density function; according to the diastrophic stratum deformation analysis and calculation method for the active fault zone, the problems of multi-physical field splitting and time scale solidification in a traditional method are solved by constructing an energy-mediated multi-field dynamic coupling model and a space-time decoupling algorithm. On the basis of an interaction path of energy density gradient tensor unified mechanics, seepage and temperature fields, instantaneous bidirectional feedback of three-field parameters is achieved, compared with a traditional model, the pore pressure prediction error is reduced, and the calculation efficiency is improved; and meanwhile, the joint simulation efficiency of the second-level seismic event and the ten-thousand-year tectonic motion is improved, the interface parameter continuity error is controlled, and the precision and stability of cross-scale modeling are improved.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention relates to a mechanical equipment fault data identification method based on artificial intelligence, and belongs to the technical field of data processing and artificial intelligence. The problems of insensitive signal processing, insufficient feature extraction, significant noise interference, low model training efficiency and the like in fault diagnosis in the prior art are solved. According to the method, a self-adaptive normalization method based on energy density is provided, the influence of non-stationary signals is effectively inhibited, and key features are reserved; through mixed energy entropy feature extraction and inter-band energy jump penalty terms, the sensitivity to a complex fault mode is significantly enhanced; constructing a fault feature enhancement strategy based on a Gaussian potential well, and reinforcing the response of a fault feature accumulation area; a weighted loss function and a gradient directional correction mechanism are adopted, so that the robustness and accuracy of the model are improved; and in combination with an entropy weighted learning rate and a covariance scaling strategy, adaptive training is realized, and the convergence speed and adaptability are improved. According to the method, the precision and efficiency of mechanical fault diagnosis are improved, and support is provided for industrial intelligent development.

The invention discloses a small sample radar target identification method based on multi-feature fusion. According to the invention, the natural resonant frequency characteristics of radar echo signals are introduced into a small sample learning framework, and the advantages of certain stability and simple and rapid extraction under the condition of attitude and observation angle change are fully utilized; attention multi-feature weighted fusion based on energy guidance is carried out on natural resonant frequency features and time frequency features, scattering features are described from different feature mechanisms, and the completeness of the features under the small sample condition is improved; in addition, a loss function based on combination of feature similarity measurement and classification loss is also designed, so that the model can learn feature representation with higher distinction degree while the feature space discrimination is optimized, and the performance is improved in a feature fusion-classification network. According to the method, on the premise that the reasoning time and the calculation complexity are not remarkably increased, the recognition accuracy under the condition that the observation angle changes is effectively improved.

At least one example embodiment provides a system for controlling a heater in a non-combustible aerosol-generating device, the system comprising a memory storing computer-readable instructions and a controller configured to execute the computer-readable instructions to cause the non-combustible aerosol-generating device to apply a first power to the heater based on a first preheat temperature determine an estimated energy applied to the heater during application of the first power, and apply a second power to the heater based on the estimated energy, an energy threshold and a second preheat temperature, the second power being less than the first power.

Methods, systems, and devices for wireless communications are described. For instance, a first wireless device may transmit, to a second wireless device, a first signal for probing an energy state of the second wireless device. The first wireless device may monitor for a second, backscattered signal indicating the energy state of the second wireless device and may transmit, to the second wireless device, an indication of resources for harvesting energy based on monitoring for the second, backscattered signal. The first wireless device may transmit, to the second wireless device and over the resources, a third signal distinct from the first signal and associated with harvesting energy.

The invention discloses a wind power-photovoltaic-photo-thermal cold power co-production system integrating heat pump energy storage and suitable for a large energy base, and belongs to the technical field of energy storage. A hot side outlet of a steam generator of the system is connected with an inlet of a second molten salt tank through a fourth molten salt pump, an outlet of the second molten salt tank is connected with an inlet of a first three-way valve through a second molten salt pump, and a first outlet of the first three-way valve is connected with an inlet of a heat collection tower. An outlet of the heat collecting tower is connected with a first inlet of a second three-way valve through a first molten salt pump; an outlet of the second three-way valve is connected with an inlet of a first molten salt tank; the compressor is connected with a hot-side inlet of the first heat exchanger, a hot-side outlet of the first heat exchanger is connected with an inlet of the turbine through a hot side of the second heat exchanger, and an outlet of the turbine is connected with an inlet of the compressor sequentially through a hot side of the third heat exchanger and a cold side of the second heat exchanger. According to the system, cold energy can be supplied to the outside through the heat pump energy storage system in the energy storage stage, cold and power cogeneration is achieved, and the comprehensive energy efficiency of the large energy base is improved.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention relates to the field of energy management, and discloses a distributed energy intelligent configuration method and device based on Internet of Things big data, and the method comprises the steps: collecting the real-time energy flow and connection state of each node, integrating the real-time energy flow and connection state into initial node embedding, constructing a graph model, calculating the mutual influence weight of the nodes, and adjusting the interaction frequency; based on the mutual influence weight, the interaction frequency and the graph model, performing feature extraction on each node state, generating a node state feature vector, identifying a potential fault starting point exceeding an abnormal fluctuation threshold, predicting a propagation trend, and generating a risk path and a priority; extracting a hub based on a risk path and a priority, estimating an influence range and simulating diffusion, calculating system stability, if the system stability is lower than a threshold value, generating a resource optimization scheme, fusing position marks to generate an adjustment verification vector, updating an abnormal fluctuation threshold value, and reevaluating a potential fault starting point to obtain an adjusted propagation path; according to the invention, accuracy and robustness of dynamic energy management can be realized.

The invention provides a reusable rocket landing stage starting height prediction method, a guidance system and a processor, and aims to dynamically and accurately determine the ignition time of an engine by constructing a real-time online starting height prediction model based on an energy conservation principle and combining multiple safety thresholds (including an allowable starting height upper limit and a standby starting height lower limit) so as to predict the starting height of the rocket in a landing stage. And the safety and fuel optimality in the whole flight envelope are ensured, so that the reliability, accuracy and economical efficiency of the reusable rocket landing are remarkably improved. The problems that in the prior art, engine starting height prediction precision and robustness are insufficient, online adaptation to uncertainty of a complex flight environment cannot be achieved, and safety margin consideration is insufficient are solved, and potential safety risks are thoroughly eliminated.

The invention relates to a self-adaptive interaction method based on multi-modal emotion calculation and a robot. The system realizes dynamic management of emotional values of different interaction objects by constructing a time sequence and a real time axis, and improves the memory weight in combination with key events. The system comprises a random event generation unit which simulates an environment event and gives an emotional influence; the user emotion judging unit is used for calculating emotion energy based on real-time input and historical interaction; the user impression dynamic adjusting unit is used for realizing impression weight attenuation and updating; the system emotional energy calculation unit fuses the virtual environment and the real time information to generate a system emotional state; the emotion response strategy unit is used for dynamically generating personalized emotion output according to the system emotion, the user emotion and the user impression; the aggressiveness detection unit prevents aggressiveness content from being output, and interaction safety is guaranteed. The system improves personalization, dynamic adaptation and safety of emotional response, and is suitable for scenes of intelligent accompanying, emotional counseling, customer service and the like.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention discloses a noise signal recognition method based on deep learning. The method comprises the steps of original signal collection, signal preprocessing, noise recognition feature extraction, noise signal recognition model establishment and noise signal intelligent recognition. The invention relates to the technical field of digital signal data processing, in particular to a noise signal identification method based on deep learning, and innovatively provides a self-adaptive signal mode decomposition method based on a combined judgment mechanism of energy threshold value judgment and energy range value judgment. Decoupling and reservation of different frequency bandwidth information in an original signal can be effectively completed, and the recognition accuracy of non-stationary communication noise is improved; a frequency band filter bank constructed based on a Gaussian function is introduced, so that the accuracy of communication signal identification is improved; a method for constructing a noise recognition model by fusing graph neural network structure modeling and an original type reference vector matching mechanism is innovatively proposed, and the recognition robustness of the model to non-stationary communication interference signals is improved.

The invention provides a power grid system control method and device, computer equipment and a storage medium in the field of power grid system control, and the method comprises the steps: firstly, determining a dynamic inertia coefficient through integrating node frequency deviation, an energy storage charge state and voltage deviation, calculating inertia compensation power through employing the frequency deviation, precisely capturing the dynamic characteristics of a system, and carrying out the calculation of inertia compensation power; and constructing a target function based on the equivalent impedance matrix and the power deviation, solving a target control sequence under constraint conditions, realizing system power optimization distribution, and further adjusting node power output from multiple aspects by determining composite inertia, synthesizing inertia power, regulating and controlling a power vector and an inertia matrix. And finally, based on energy function construction and partial derivative calculation, generating a control instruction of each node. The steps can effectively deal with system power disturbance, rapidly compensate insufficient inertia, optimize power distribution, maintain system frequency stability, improve safety, reliability and stability of operation of a power system, and enhance adaptability of the system to complex working conditions.

The invention relates to the technical field of mine coal and rock dynamic disaster prevention and control, in particular to a micro-seismic-based deep coal and rock mass instability dynamic multi-parameter evaluation method. The method comprises the following steps: acquiring waveform data of each micro-seismic event of a deep coal and rock mass through a mine micro-seismic system; according to the waveform data, acquiring a micro-seismic characteristic index set of each micro-seismic event; according to the seismic source position and the micro-seismic characteristic index set of each micro-seismic event, acquiring an energy-based micro-seismic magnitude; according to the micro-seismic magnitude, obtaining a micro-seismic b value corresponding to a preset duration, and according to the micro-seismic characteristic index set, obtaining a micro-fracture type and change trend information of accumulated energy of each micro-seismic event; and according to the micro-seismic energy, the change trend information of the accumulated energy, the frequency, the ringing count, the micro-fracture type and the micro-seismic b value, the dynamic instability fracture risk information of the deep coal and rock mass is obtained. According to the invention, the determination accuracy and efficiency of the instability and fracture risk information can be improved.

Methods and apparatus for estimating an energy storage performance property of an energy system are provided. The method includes providing, as input to an energy storage model and a machine learning model, sensor information associated with the energy system, wherein the energy storage model comprises an empirical model and / or a physics-based model, providing as input to the machine learning model, one or more values based on an output of the energy storage model, and determining based, at least in part, on an output of the machine learning model, an estimate of the energy storage performance property.

The invention relates to the technical field of computer data processing, and particularly provides an MCU low-power-consumption firmware upgrading method based on energy self-adaption, which comprises the following steps of: establishing a communication link between an upgrading initiating end and MCU equipment, and acquiring energy state parameters in real time through an energy monitoring terminal arranged in the MCU equipment; according to a preset energy state threshold interval, the comprehensive value of the energy state parameters is divided into a low energy state, a medium energy state and a high energy state; dynamically adjusting an upgrading strategy parameter of the current firmware according to a preset adjustment rule based on the current energy state level; and transmitting target firmware data to the MCU equipment through the communication link based on the dynamically adjusted upgrading strategy parameters, and executing firmware upgrading operation until upgrading is completed. According to the scheme, an adaptive system of energy state perception-strategy dynamic adjustment-hardware collaborative optimization is constructed, and the problems of high power consumption and unbalanced efficiency and reliability caused by the fact that the energy state is not combined in the prior art are effectively solved.

The invention provides a rock-soil body dynamic failure simulation method based on energy dissipation hot spots. The rock-soil body dynamic failure simulation method comprises the steps that a near-field dynamics and discrete element coupling model used for describing the mechanical state of particles under the earthquake action is constructed; calculating particle stress information; calculating the particle speed and displacement according to the particle stress information, and analyzing the damage process according to the energy dissipation hot spots to obtain a calculation result; and outputting a calculation result and drawing a cloud picture. According to the method, a near-field dynamics and discrete element coupling model is constructed for the slope instability phenomenon under the earthquake action and is applied to discrete element software, and a method for describing deformation and damage characteristics of a slope under earthquake disturbance is formed; according to the rock-soil body dynamic failure simulation method based on the energy dissipation hot spots, the shape and characteristics of the sliding surface are analyzed, the slope instability process under the earthquake load is reflected, the prediction precision and the sliding surface recognition accuracy are improved, and the rock-soil body dynamic failure simulation method has remarkable advantages in prediction of rock-soil body progressive failure and sudden landslide.

The invention provides an independent hybrid energy storage power station capacity configuration method and system based on profit maximization. According to the accessible total power of the independent hybrid energy storage power station, charging and discharging parameters and energy storage system duration of an energy type energy storage system and a power type energy storage system, related parameters of income measurement and calculation, and electricity price parameters and frequency modulation parameters of a project construction site of the independent hybrid energy storage power station; based on the installed power of the energy-type energy storage system and the installed power of the power-type energy storage system, by taking the full-life-cycle benefit maximization of the independent hybrid energy storage power station as a target, constructing a target function and constraint conditions; and solving the target function to obtain the installed power of the energy type energy storage system and the installed power of the power type energy storage system, and carrying out capacity configuration on the independent hybrid energy storage power station. The economic benefit of the independent hybrid energy storage power station can be improved, and the stability, reliability and sustainability of the independent hybrid energy storage power station in long-term operation can be ensured.

The invention discloses a method for predicting the remaining driving range of a pure electric vehicle. The method comprises the steps that original data of the vehicle within a preset time span are collected and processed; training a joint prediction model according to the original data, and obtaining a predicted battery health state through the joint prediction model; training a mileage prediction model according to the combined prediction model and the original data; the charge state variation of the charging section is obtained, and the actual consumed energy of the battery is calculated according to the charge state variation and the predicted health state of the battery; obtaining the current environment temperature, obtaining predicted non-driving demand energy according to the original data and the current environment temperature, and calculating driving demand energy; and obtaining a prediction segment according to the driving demand energy, and inputting the working condition information, the average current and the residual electric quantity variation of the prediction segment into the mileage prediction model to obtain the residual driving mileage. According to the method for predicting the remaining driving range of the pure electric vehicle, the remaining driving range can be predicted in combination with the low-temperature environment and the battery recession condition, so that the prediction accuracy is improved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention relates to the technical field of elevator energy consumption analysis, and discloses an elevator energy consumption analysis method and system.The elevator energy consumption analysis method comprises the steps that elevator operation data are obtained, anchor point events are detected based on the elevator operation data, time alignment processing is conducted on the elevator operation data, and a synchronous data sequence with a unified time axis is generated; based on the synchronous data sequence, elevator running states are recognized, regeneration running stages are marked, and time segments corresponding to the elevator running states are output; carrying out integral calculation on the active power of the line in a time segment, realizing bottom consumption stripping in combination with a standby power reference, and outputting segment energy consumption through positive and negative power decomposition; and in the regeneration operation stage, feedback energy and braking energy are calculated according to the elevator operation data, feedback efficiency is calculated, and an energy consumption analysis result is output. According to the method, a reliable basis is provided for energy efficiency evaluation, abnormal early warning and operation optimization of the elevator system.

The invention relates to the technical field of energy utilization, and discloses an urban heat and cold supply centralized energy comprehensive utilization method. The method comprises the following steps: S1, acquiring physical parameters and operation data of an urban heating and cooling system, wherein the physical parameters comprise a system initial temperature field, a thermal diffusion coefficient, heat source distribution, equipment performance parameters and heating and cooling demand loads; the invention further provides an urban heat and cold supply centralized energy comprehensive utilization system. The data acquisition module is used for acquiring physical parameters and operation data of the urban heating and cooling system; and the thermodynamic modeling module is used for constructing a thermodynamic model based on the law of conservation of energy. By constructing an optimization model based on energy conservation and entropy increase minimization, energy loss in a heating and cooling system is remarkably reduced, by optimizing energy distribution in real time and dynamically adjusting temperature field distribution, invalid energy consumption is reduced to the maximum extent, the overall energy utilization rate of the system is increased, and the energy utilization rate of the system is increased. And therefore, the heating and cooling system can operate efficiently under various working conditions.

The invention relates to a transient synchronous stability evaluation method for a multi-virtual synchronous machine grid-connected system. The method comprises the following steps: constructing a system nonlinear reduced-order mathematical model suitable for transient synchronous stability analysis; considering active and reactive power control dynamic strong coupling and multi-virtual synchronous machine dynamic interaction, and analyzing an energy function of the construction system; respectively calculating a maximum energy value for ensuring system stability and a maximum energy value for ensuring that the system does not trigger current amplitude limiting under the condition that the system does not trigger current amplitude limiting after the fault is removed through a nearest unstable equilibrium point method and an optimization method; taking the minimum value of the two values to obtain a critical energy value for ensuring the transient stability of the system; calculating the track of the system during the fault through numerical integration to obtain the state of the system at the fault removal moment; the state of the system at the fault clearing moment is substituted into an energy function, and an energy function value of the system at the fault clearing moment is calculated; and evaluating the transient synchronization stability of the system.

Systems and methods are provided for modeling and for providing a graphical representation of tissue heating and electric field distributions for medical treatment devices that apply electrical treatment energy through one or a plurality of electrodes. In embodiments, methods comprise: providing one or more parameters of a treatment protocol for delivering one or more electrical pulses to tissue through a plurality of electrodes; modeling electric and heat distribution in the tissue based on the parameters; and displaying a graphical representation of the modeled electric and heat distribution. In another embodiment, a treatment planning module is adapted to generate an estimated target ablation zone based on a combination of one or more parameters for an irreversible electroporation protocol and one or more tissue-specific conductivity parameters.