154 results about "Learning factor" patented technology

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention provides an energy optimal path planning method and device for an unmanned surface vessel in a complex marine environment, and belongs to the technical field of path planning. The method provided by the invention comprises the following steps: constructing a comprehensive energy consumption model; constructing a hybrid enhanced particle swarm optimization algorithm; constructing a safety space set and an adjacent graph through a grid method, generating an initial optimal path by adopting an A * algorithm, and carrying out bounded random disturbance and safety correction on target particle waypoints; smoothing the path generated by iteration by adopting a B-spline technology, resampling the smoothed optimal solution, and then reinjecting the optimal solution into the population; adjusting an inertia weight and a learning factor based on the number of iterations, and introducing double guide factors to carry out secondary adjustment on a cognitive item of particle speed updating; and embedding the comprehensive energy consumption model as a fitness function into a hybrid enhanced particle swarm optimization algorithm, performing real-time energy consumption evaluation, individual and global optimal path updating and path smooth optimization on each candidate path in an algorithm iteration process, and outputting an optimal navigation path of the unmanned surface vessel.

The invention discloses a generative adversarial network architecture search method and system based on a GA-PSO hybrid algorithm, and belongs to the technical field of deep neural networks. The method constructs a generative adversarial network super-network architecture, globally explores an architecture population by using a genetic algorithm, and performs local parameter adjustment on an elite architecture in combination with particle swarm optimization; the inertia weight and the learning factor are dynamically balanced, and framework compliance is ensured through discretization coding constraint; and finally, based on Pareto optimality, integrating a multi-target evaluation result and a primary and secondary collaborative proportion loop optimization solution set. Compared with traditional architecture search, the method has the advantages that the premature convergence problem is solved through a hybrid optimization mechanism, global and local collaboration is achieved in combination with weight sharing evaluation and hierarchical coding, and the search period is remarkably shortened while the generation quality is improved.

The invention provides an improved particle swarm optimization (PSO) algorithm for mechanical arm trajectory planning, and aims at solving the problems that a traditional PSO algorithm is low in convergence speed and prone to falling into a local optimal solution. Therefore, the inertia weight and the learning factor of the algorithm are dynamically adjusted, so that the inertia weight and the learning factor change along with the increase of the number of iterations, and the global search capability and the convergence speed are enhanced; meanwhile, an elite reverse learning (EL) strategy is introduced, an optimal trajectory planning scheme is selected according to a fitness function, a new search area is explored through reverse particles, and the local search capability is enhanced; and a Gaussian-Cauchy variation (GC) strategy is combined, so that the global search capability is further improved, and a local optimal solution is avoided. In the optimization process, a single-target optimization method is adopted to carry out hierarchical classification on population solutions, and the performance of the algorithm under single-target optimization and constraint conditions is improved. Finally, the trajectory of the mechanical arm is optimized based on the algorithm, and particularly, each time period in a 3-5-3 polynomial interpolation method is finely optimized, so that the time from an initial point to a target point is shortened, a trajectory planning scheme with the optimal time is obtained, and the working efficiency of the mechanical arm is improved.

A method and system are provided for identifying time-varying suspension characteristics of heavy-load vehicles. The method includes collecting sequential control state data of a mining truck using sensors, predicting parameter-related factors through a deep learning network, estimating suspension stiffness and damping coefficients via a linear dynamic model considering longitudinal-vertical coupling, and predicting future system states through a nonlinear dynamic model based on the estimated parameters and learned factors. According to the method, a deep learning network is integrated into a physical model of the mining truck, an accurate longitudinal-vertical dynamical model of the mining truck is established, accurate suspension parameters are identified, the stiffness damping time-varying characteristics of the suspension of the mining truck are given through a physical model-data driving method, and the model has certain interpretability and generalization; the rigidity and damping of the four suspensions can be obtained only through sprung information.

The invention provides a public security patrol path planning method and system, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting patrol action information of a plurality of unmanned aerial vehicles in a public security patrol process, the patrol action information comprises a path node passed by each unmanned aerial vehicle, a real-time motion state, a public security risk area and historical operation data; constructing a particle swarm evolution function according to a patrol path formed between path nodes, the real-time motion state and the historical operation data; generating a plurality of candidate public security inspection paths through a path planning fitting function based on path base points corresponding to the inspection paths and position parameters of the public security risk area, and generating a plurality of particles based on the plurality of candidate public security inspection paths through a particle swarm evolution function, each candidate public security inspection path corresponds to one particle in the particle swarm evolution function; and performing iterative optimization processing on all particles generated in the particle swarm evolution function through the particle swarm evolution function, the first learning factor and the second learning factor to obtain an optimal particle, the optimal particle representing an optimal public security patrol path. According to the invention, the positioning accuracy of the public security inspection path is improved.

The invention discloses an inter-Doppler interference suppression method based on a Gaussian learning factor particle swarm algorithm. The inter-Doppler interference suppression method comprises the following steps that pilot frequency and data symbols are coded to a resource grid of a two-dimensional time delay Doppler domain through orthogonal time-frequency space modulation; on a transmitter, mapping data on a time delay Doppler domain grid to a time-frequency domain through inverse symplectic finite Fourier transform to obtain a time-frequency domain signal; performing Hisenberg transformation on the time-frequency domain signal to obtain a time domain sending signal; obtaining a time domain receiving signal after the time domain sending signal passes through a wireless channel; performing Doppler compensation processing on the time domain receiving signal by using Doppler frequency shift to obtain a signal after Doppler compensation; then performing matched filtering on the signal after Doppler compensation and a received pulse, and sampling to obtain a matched filter output signal; and the matched filter output signal is subjected to the Sextile Fourier transform to obtain a time delay Doppler receiving signal. The method can realize suppression of inter-Doppler interference to a certain extent.

The invention belongs to the technical field of new energy power generation and grid connection, and particularly relates to a wind power plant electric energy quality optimization method and system based on an improved particle swarm algorithm. Aiming at the problems of voltage fluctuation, frequency deviation and high harmonic content during grid connection of a wind power plant, a wind power plant simulation model is built through Matlab / Simulink, a multi-target fitness function is built according to the voltage deviation, the frequency deviation and the total harmonic distortion (THD), the self-adaptive inertia weight and learning factors of a particle swarm optimization algorithm are improved, and a wind power plant grid connection model is built. And dynamically adjusting voltage adjustment gain, reactive compensation amount and filter parameters. According to the embodiment, compared with a traditional method, the number of iterations is reduced by 40%, the voltage fluctuation suppression ratio is improved by 10%, the frequency adjusting time is shortened to be within 0.3 second, the THD is reduced to 2.1% from 5.2%, and the power grid stability and the electric energy quality are improved.

The invention belongs to the technical field of array antenna synthesis, and particularly relates to a thin cloth rectangular planar array antenna synthesis method, a program, equipment and a storage medium. According to the method, a thin cloth rectangular planar array antenna comprehensive method is converted into an optimization problem of an array element position in a quadrant, an improved sparrow algorithm is adopted, Kent mapping population initialization is introduced, and diversity of an initialized population is enhanced; the idea of a sine and cosine algorithm is fused in a position updating strategy of a discoverer, and a nonlinear sine learning factor is introduced to enhance all search capabilities; and finally, a crisscross strategy is introduced into a sparrow algorithm, and the optimization precision is improved by improving a search mode of a supervisor.

The invention provides a sound source localization method based on an improved particle swarm algorithm. Comprising the following steps: selecting a sound source positioning scene, acquiring a measurement data set by using a microphone array, and establishing a mathematical model; initializing the positions and speeds of the particles, and calculating the fitness value of each particle to evaluate the distance between the current position of the particle and the target solution; setting an inertia weight to control the influence of the current speed of the particles on the new speed, and setting an individual learning factor and a social learning factor to control and coordinate the behaviors of the particles in the search space; setting an iteration control condition, combining with a hybrid optimization algorithm, and utilizing a boundary processing strategy to carry out iteration updating of particles; and after iteration updating is completed, error analysis is performed on a sound source positioning result, and the particle swarm optimization algorithm is continuously optimized by using an optimization strategy according to an analysis result. The method can effectively improve the sound source positioning precision and algorithm performance, is suitable for a complex acoustic environment, and provides an efficient and feasible solution for the application in the acoustic field.

The invention discloses a robot energy consumption optimization method based on an improved particle swarm algorithm, and the method comprises the steps: building a space rectangular coordinate system through a robot base, and carrying out the kinematics and dynamics modeling of the joint parameters of a robot; the pose of the end effector is converted into a joint angle sequence through an inverse solution algorithm, interpolation is conducted on the sequence through a polynomial spline function, and a robot joint movement track is determined; constructing a robot energy consumption optimization model; based on a robot joint motion trail and a robot energy consumption optimization model, the energy consumption of the robot is used as a fitness function of a particle swarm algorithm, and key motion parameters in the motion process of the robot are used as particles of the algorithm in combination with dynamically adjusted inertia weights and learning factors. The energy consumption of the robot in the moving process is reduced by updating the speed and the position of the particles. The energy consumption is reduced to the maximum extent while the task completion quality and the energy consumption calculation accuracy are ensured, and the system efficiency is improved.

The invention discloses a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm. The method comprises the steps of obtaining charge and discharge parameters of a power distribution network containing distributed energy storage batteries, and establishing a first objective function based on the charge and discharge parameters of the power distribution network containing the distributed energy storage batteries, the objective function comprises a power distribution network voltage deviation value and the current-day operation cost; establishing constraint conditions based on the first objective function, wherein the constraint conditions comprise storage battery SOC and power constraint and node power balance constraint; and constructing an improved particle swarm algorithm containing an inertia weight factor and a learning factor, and solving the first objective function according to the improved particle swarm algorithm. The operation efficiency of the power distribution network is improved, the operation cost is reduced, and meanwhile the voltage stability of the power distribution network and reasonable charging and discharging of the energy storage battery are ensured. Through the optimization algorithm, the charging and discharging states of the energy storage battery can be effectively balanced, the service life of the battery is prolonged, and the maintenance cost is reduced.

The invention belongs to the related technical field of temperature control, and discloses a fuzzy PID temperature control method and system based on an improved PSO algorithm, and the method comprises the steps: S1, constructing a temperature control system comprising a fuzzy PID controller; s2, parameters of a fuzzy PID controller are optimized based on an improved PSO algorithm; s3, performing temperature control by using the optimized fuzzy PID controller; s2 comprises the steps of S21, setting algorithm parameters and initializing a population; s22, updating the speed and position of particles in the population in each iteration, and calculating a fitness value; when the speed and the position of the particles in the population are updated, the inertia weight and the learning factor are updated by adopting a nonlinear decline strategy; and S23, when the optimal fitness value of the group is smaller than a preset fitness value or the maximum number of iterations is reached, ending the algorithm. According to the invention, the POS algorithm is improved, the global search capability and the iteration efficiency are improved, and the optimized fuzzy PID control can ensure the temperature control precision.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention discloses a laser wireless energy transfer dynamic MPPT control method based on an RL-PSO hybrid algorithm and a reconfigurable array system, and aims to solve the problems of power oscillation and mismatch loss caused by light spot offset of a photovoltaic array under laser dynamic irradiation. According to the method, reinforcement learning (RL) and particle swarm optimization (PSO) are fused, dynamic parameters such as the light spot moving speed and irradiance are sensed in real time through the RL, the inertia weight and learning factors of the PSO are adjusted in a self-adaptive mode, and collaborative optimization of global search and local tracking is achieved. According to the system level, a reconfigurable photovoltaic array is designed, and according to data of a light spot position sensor, battery series-parallel topology is dynamically adjusted through a switch matrix, and current matching is optimized. According to the method, double-loop control is adopted, wherein an outer-layer RL decision module dynamically switches PSO working modes according to the environment, and an inner-layer PSO module optimizes the working voltage. The system efficiency in a dynamic scene can be improved by 15%-30%, mismatch loss is reduced by 40% or above, and the method is suitable for unsteady-state energy supply scenes such as unmanned aerial vehicles and mobile robots.

The invention relates to the technical field of heat supply unit peak regulation capacity optimization, in particular to a heat supply unit heat storage peak regulation strategy construction method and device and a storage medium, and the method comprises the steps: carrying out the hourly prediction of a day-ahead electrical load and a day-ahead thermal load through a target BO-ResNet-AM neural network prediction model, obtaining a day-ahead hourly electric load predicted value and a day-ahead hourly thermal load predicted value; based on a power supply balance constraint, a heat supply balance constraint, a unit ramp rate constraint and a target function, taking pipe network heat storage / release, actual power supply power and actual heat supply as decision variables, and constructing an initial source network load storage collaborative interaction mode optimization scheduling model; and converging the initial source network load storage collaborative interaction mode optimization scheduling model to a globally optimal solution through an adaptive weight learning factor particle swarm optimization algorithm. By accurately predicting the electric heating load and quantifying the heat storage capacity of the pipe network, peak clipping and valley filling of the electric load and space-time translation of the thermal load are achieved, and the deep adjustment and peak capacity of the unit is improved.

The invention discloses a distributed power supply-containing power distribution network self-healing method and system based on defect identification, and the method comprises the steps: inputting operation data into a defect identification model, obtaining a defect identification result, correcting a feeder load rate and a weighted structure entropy according to the defect identification result to generate a candidate solution set, inputting the candidate solution set into an upper layer optimization model, and obtaining a defect identification result; taking the maximization of the load recovery amount in the island as a target, combining with a line medium to guide a search direction and adopting a second-order cone relaxation method to carry out optimization solution to obtain a load recovery scheme in the island, and inputting the candidate solution set and the load recovery scheme into a lower-layer optimization model to obtain a load recovery scheme in the island; carrying out iterative solution under a constraint condition by combining an improved star-sparrow optimization algorithm which introduces an inertia weight and a learning factor, and obtaining a load recovery result after fault reconstruction; and determining a target switch operation sequence and a load recovery strategy based on the load recovery result to perform self-healing of the power distribution network so as to improve the self-healing capability and the operation reliability of the power distribution network.

The invention relates to the technical field of hydraulic structure service behavior analysis and computational mechanics, and particularly discloses a high arch dam deformation behavior fractional order numerical analysis physical and mechanical parameter inversion method and module. A viscoplasticity parameter inversion method is provided based on a Levenberg-Marquardt method by using rheological test data. Optimizing to obtain a to-be-inverted parameter sample combination by means of central composite design; constructing an elastic and viscoelastic parameter inversion objective function in combination with deformation in-situ monitoring data; based on a multiple-input-multiple-output support vector machine, establishing an elastic and viscoelastic parameter inversion agent model; a random inertia weight, an asynchronous change learning factor, golden sine disturbance and differential evolution variation disturbance are introduced to improve a particle swarm algorithm, so that a parameter optimal inversion value search strategy is established. The method aims at improving the accuracy and objectivity of numerical analysis of the deformation state of the high arch dam, provides technical support for in-service engineering safety monitoring and dangerous case early warning, and has wide application prospects and markets.

The invention relates to a micro-grid multi-objective optimization scheduling method and device, and belongs to the technical field of micro-grid optimization scheduling, and the method comprises the following steps: determining the structure of a micro-grid system, and collecting the equipment parameters of each distributed power supply; establishing a micro-grid optimization scheduling model according to the characteristics of the micro-grid and the collected equipment parameters, wherein the micro-grid optimization scheduling model takes minimization of user operation cost and environment cost as an objective function; generating an initial population of the MOPSO algorithm by using a chaotic equation, and improving the value of a learning factor by using a dynamic adjustment parameter to obtain an improved MOPSO algorithm; and performing optimization solution on the micro-grid optimization scheduling model by using an improved MOPSO algorithm to obtain the output power of each distributed power supply. According to the invention, the cost and environmental factors of the micro-grid are considered at the same time, and effective scheduling and management of the micro-grid are realized.

The invention provides a wind power networking type VSG control adaptive parameter optimization method, and the method comprises the steps: firstly, determining a constraint relation between a virtual inertia and a damping coefficient based on the system performance of a wind power networking type VSG; based on the constraint relation between the virtual inertia and the damping coefficient, an adaptive parameter optimization module is established, and based on the adaptive parameter optimization module, a particle swarm optimization algorithm of fuzzy logic is introduced, and values of the virtual inertia and the damping coefficient are iteratively optimized by dynamically adjusting an inertia weight and a learning factor in the particle swarm optimization algorithm. And finally outputting the optimal combination of the virtual inertia and the damping coefficient. The virtual inertia and the damping coefficient can be flexibly adjusted according to the dynamic change of the operation condition of the power grid, and the stability and the electric energy quality of the power grid are improved; meanwhile, adjustment of the virtual inertia and the damping coefficient considers the constraint relation between the virtual inertia and the damping, the virtual inertia and the damping coefficient are effective at the same time, and the condition that the parameter optimization effect is limited is avoided.

The invention belongs to the field of dam body stability evaluation, and provides a dam body stability evaluation method based on a multi-strategy improved particle swarm optimization algorithm, which comprises the following steps: S1, collecting and preprocessing various monitoring data for dam stability evaluation; s2, establishing a multi-physics field coupling dam model; s3, defining an objective function and constraint conditions of dam body stability evaluation; s4, based on an MSIPSO algorithm, fusing hybrid initialization, adaptive inertia weight and learning factor, dynamic disturbance and re-initialization, multi-subgroup coevolution and a multi-target constraint processing mechanism, and constructing a multi-strategy improved particle swarm optimization algorithm; s5, iteratively optimizing a to-be-evaluated parameter combination in the multi-physics coupling dam model by using the MSIPSO algorithm, inputting optimized parameters into the model for coupling analysis, and calculating a fitness value according to model output feedback so as to update a particle state of the MSIPSO algorithm; and S6, carrying out dam body stability evaluation and risk evaluation based on the optimal parameter combination obtained through optimization of an MSIPSO algorithm and the dam response.

The invention relates to a seismic oscillation fitting method matched with a multi-damping-ratio response spectrum. According to the seismic motion fitting method, the seismic geology environment of the region is classified and the feature mother wave set is constructed, so that the seismic geology features of the region can be fully considered, and the fitting result can better conform to the actual condition of the target region and is more targeted and accurate. Through combination of characteristic seismic oscillation mother wave optimization and a dynamic parameterized particle swarm optimization algorithm, aiming at nonlinear characteristics of multi-damping ratio spectrum coupling matching, an improved particle swarm optimization PSO algorithm with a self-adaptive inertia weight and a learning factor is designed, and while the multi-target response spectrum fitting precision is improved, the multi-target response spectrum fitting precision is improved. And the time-frequency non-stationary characteristic of original seismic oscillation is reserved.

The invention relates to an electronic paper display mapping method based on visual perception and dynamic clustering, and belongs to the technical field of display. According to the method, firstly, learning factors and particle weights of a particle swarm optimization algorithm are dynamically adjusted, a gray segmentation threshold is optimized in combination with K-means clustering, and the accuracy and convergence efficiency of gray distribution are remarkably improved; secondly, establishing a dynamic error diffusion system based on visual perception, designing a frequency domain visual weighted filter and a spatial domain sensitivity function by combining a human visual perception theory, dynamically compensating quantization errors and enhancing edge details; and meanwhile, a Floyd-Steinberg error diffusion algorithm is improved, and a double image effect is inhibited in combination with a snakelike scanning path. According to the electronic paper display mapping method based on visual perception and dynamic clustering, the electronic paper image display quality is effectively improved, particularly when a high-gray-scale image is displayed, details can be reserved to the maximum extent, and the problems of gray-scale distortion and edge blurring in traditional electronic paper display are solved.

The invention relates to an ocean communication method based on self-adaptive Q-learning factor setting. In the method, each node periodically collects own state information and broadcasts the own state information to surrounding nodes, and after each node receives a message and stores the message in a cache region, routing decision and parameter adjustment processes are circularly executed until the message is completely transmitted to a target node to complete communication; wherein the routing decision and parameter adjustment process comprises the following steps of: calling a message from a corresponding buffer area by a current node, selecting a next forwarding node of the message based on a current Q value table, updating a learning rate and a discount factor based on the current state information of the node after sending the message, and performing routing decision and parameter adjustment by combining a reward function, the updated learning rate and the discount factor. And a new Q value table is obtained through asynchronous updating of the double-Q network. Compared with the prior art, the method has the advantages that the delivery rate is higher, the method can better adapt to changeable environments, and the problem of real-time changes of topology, load and channel quality can be well solved.

The invention provides a task unloading and resource allocation method in a networked automobile scene, and the method comprises the steps: S1, constructing an Internet of Vehicles mobile edge computing system model, and receiving a semantic information task sent by each user vehicle; s2, constructing a priority evaluation model, and evaluating each semantic information task based on task urgency and task importance to obtain a task priority; s3, processing each semantic information task to obtain a task processing result, and returning the task processing result to the corresponding user vehicle; s4, constructing a comprehensive utility function as a fitness function of the model; and S5, introducing a particle swarm algorithm, defining particles to represent bandwidth and task allocation, and introducing an inertia factor and a learning factor to dynamically regulate and control a particle iteration updating process so as to guide the particles to search an optimal solution and obtain an optimal task unloading and resource allocation scheme. The method has the beneficial effects that efficient optimization of task unloading and resource allocation can be realized, and the overall performance and reliability of the system are improved.

The invention discloses a charging pile intelligent management system based on big data. The system comprises a data acquisition module, a data preprocessing module, a charging pile load prediction module, a charging pile controllable load adjustment module, a model hyper-parameter optimization module and a charging pile intelligent management module. The invention relates to the technical field of charging pile data processing, in particular to a charging pile intelligent management system based on big data, according to the scheme, a charging pile load prediction module and a charging pile controllable load adjustment module are innovatively combined, the overall power grid load condition is considered, and the level of charging pile intelligent management is improved; a charging pile load prediction activation function, a momentum updating strategy and a mechanism for dynamically adjusting learning factors are designed, so that the accuracy of a prediction result is improved; and through nonlinear sine chaotic mapping, adaptive individual genetic probability and superior product retention strategy, the global optimal solution search capability is enhanced, an optimal parameter combination is obtained, and the accuracy of a model result is improved.

The invention relates to the technical field of railway bridge crack detection and positioning, in particular to a steel plate crack positioning method based on a convolutional neural network and a particle swarm optimization algorithm, which comprises the following steps: drawing grid lines in a corresponding region of a steel plate, detecting acoustic emission signals of specific coordinate positions in the corresponding region of the steel plate based on a convolutional neural network model, obtaining a steel plate crack first positioning result; wherein the convolutional neural network model adopts a global mean pooling layer to execute average pooling operation on each channel in the two-dimensional feature map after convolution processing, and outputs a one-dimensional feature vector; or introducing an adjustable learning factor and an inertia weight into a particle swarm optimization algorithm, and carrying out traversal search on the acoustic emission signals of the corresponding areas of the steel plate in combination with the crossing thought of a genetic algorithm to obtain a second positioning result of the steel plate crack; and taking the first positioning result or the second positioning result as a final positioning result of the steel plate crack. According to the invention, automatic detection and accurate positioning of bridge fatigue cracks can be realized.

The invention discloses a V2G charging and discharging scheduling method and system based on an improved backbone particle swarm algorithm, and the method comprises the steps: estimating the load of an electric vehicle, and obtaining estimation data; constructing a charging and discharging scheduling model based on the estimation data, the power grid side demand and a preset target constraint; and introducing a learning factor, and solving the charging and discharging scheduling model based on an improved backbone particle swarm algorithm to obtain a scheduling strategy. The system comprises a statistical estimation module, a model construction module and a model solving module. By using the invention, a charging and discharging strategy of the electric vehicle is provided, the electric vehicle can be ensured to access the power grid orderly, and the use efficiency and stability of the power grid are improved. The method can be widely applied to the field of resource scheduling.

The embodiment of the invention relates to the technical field of power systems and automation thereof, in particular to an alternating current and direct current hybrid power flow optimization method based on embedded direct current power flexible adjustment, and the method comprises the steps: building an embedded direct current adjustment model based on an embedded direct current power adjustment principle; constructing an AC / DC hybrid system steady-state power flow optimization model comprising a comprehensive evaluation index, a power system operation constraint and a DC operation constraint; an improved particle swarm algorithm is adopted to solve the steady-state power flow optimization model, the improved particle swarm algorithm updates a population through a chaos quasi-opposition strategy and optimizes an inertia weight and a learning factor, and a power flow optimization result is obtained; comparing different embedded direct-current power regulation modes, and verifying the effectiveness of the alternating-current and direct-current hybrid power flow optimization method; according to the method, accurate power flow optimization of the alternating-current and direct-current hybrid system in a new energy output fluctuation and load change scene is realized, and the economical efficiency, the safety and the stability of system operation are improved.

The invention discloses a control method for stabilizing broadband oscillation based on adjustment of an operation mode. The method comprises the following steps: screening key variables influencing the operation mode of a new energy station system; constructing a new energy station system oscillation stability margin analysis function according to a logarithmic derivative method; constructing an optimization problem of oscillation stability margin maximization based on the key variable and an oscillation stability margin analysis function; solving the optimization problem by adopting a particle swarm algorithm based on a dynamic inertia weight and a learning factor to obtain an operation mode with optimal oscillation stability; and the new energy station system is controlled to operate in the optimal operation mode, and the broadband oscillation stabilizing capability of the new energy station system is analyzed. According to the method, new energy consumption and oscillation risks can be considered at the same time, and the adverse effect of oscillation in a broadband range on a unit and a power grid is effectively reduced while it is ensured that the total output of the new energy unit meets the requirement.