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2463 results about "Particle swarm optimization" patented technology
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In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.
Innovative Approach to Distributed Energy Resource Scheduling
ActiveUS20140277599A1Minimal operating costProvide powerLoad forecast in ac networkWind energy generationGrid managementElectric power system
The present disclosure provides for distributed resource scheduling performed by an advanced resource scheduling (ARS) module implemented on a distributed grid management (DGM) server in a power system. The ARS module is configured to automatically generate a resource schedule for controllable distributed energy resources (e.g., resources that are remotely controllable by DGM server and ARS module) in a distribution network of the power system, such as power generation resources and energy storage resources, to provide power in a cost-effective (e.g., optimal) manner. The ARS module is configured to take into account the operating limits of the distributed energy resources (DERs), the cost curves of the DERs, the system load demand, and other operating constraints to determine the most economical operating plan for the DERs, using an optimization technique such as the particle swarm optimization (PSO) algorithm.
Owner:ORACLE INT CORP
Combined wind power prediction method suitable for distributed wind power plant
ActiveCN103268366AHigh speedHigh precisionBiological neural network modelsSpecial data processing applicationsTerrainData acquisition
The invention provides a combined wind power prediction method suitable for a distributed wind power plant. The method comprises the following steps: step 1, acquiring data and pre-processing; step 2, utilizing a training sample set and a prediction sample set which are normalized to build a wind speed prediction model based on a radial basis function neural network and predict the wind speed and variation trend of distribution fans at the next moment; step 3, building a distributed wind power plant area CFD (computational fluid dynamics) model and externally deducing the prediction wind speed of each fan in the plant area according to factors such as the terrain, coarseness and wake current influence of a distributed wind field; step 4, acquiring the power data of an SCADA (supervisory control and data acquisition) system fan of the distributed wind field; and step 5, adopting correlation coefficients. The invention firstly provides a double-layer combined neural network to respectively predict the wind speed and power. Models are respectively built through adopting appropriate efficient neural network types, and improved particle swarm optimization with ideas of 'improvement', 'variation' and 'elimination' is additionally added to optimize the neural network, so that the speed and precision of modeling can be effectively improved, and the decoupling between wind speed and power is realized.
Owner:LIAONING ELECTRIC POWER COMPANY LIMITED POWER SCI RES INSTION +2
Power distribution network double layer planning method considering the time sequence and the reliability
ActiveCN106815657AAvoid repeated traversalImproving the Efficiency of Reliability CalculationsForecastingMathematical modelNew energy
The invention relates to a power distribution network double layer planning method considering the time sequence and the reliability. The method comprises: according to the meteorological files and the load power statistical data, obtaining the typical daily power time sequence curves of the wind electricity, the photovoltaic output and the load in different seasons; based on the opportunistic constraint planning method, creating a power distribution network framework and a distributed power capacity double layer planning mathematical model, including the objective function and the constraint condition; using the particle swarm optimization algorithm to solve the model and using the minimum spanning tree algorithm to ensure the radiation and connectivity structure of the distribution network during the iterative process; and obtaining the target network framework and the Pareto optimal solution set of the distributed power capacity so as to generate the best planning scheme. The invention solves the problems that unnecessary investment into the a power distribution network incurred by the fact that a traditional power distribution network planning method containing a distributed power supply cannot reflect the typical output characteristic of a distribution type new energy; and 2) that by incorporating the power distribution network power supply reliability into a model target function, the reliability target can be realized at the planning stage.
Owner:STATE GRID FUJIAN ELECTRIC POWER CO LTD +2
Method and device for optimizing multi-constraint quality of service (QoS) routing selection
InactiveCN102158417ADiversity guaranteedFull searchData switching networksQuality of serviceRouting model
The invention discloses a method and device for optimizing multi-constraint quality of service (QoS) routing selection. The method comprises the following steps: acquiring the topological structure and link parameters of an existing network in accordance with information of a prediction model; creating a corresponding multi-constraint QoS routing model in accordance with the determined topological structure and link parameters, and constructing penalty functions to transform multi-constraint conditions, as well as constructing fitness functions for evaluating paths; using a depth-first search method to acquire initial feasible paths and initializing particle swarms; calculating the fitness value of each particle, and finding out the optimal fitness value of the particle adjacent to each particle; using the generation algorithm and the genetic algorithm-particle swarm optimization (GA-PSO) to carry out iterative solution at the beginning of the initial feasible paths, and carrying out natural selection and variation operations; and finding out paths which meet conditions and are provided with the optimal fitness values, realizing optimal routing selection under the multi-constraint condition, and executing in accordance with the found routings.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Method of Sequence Optimization for Improved Recombinant Protein Expression using a Particle Swarm Optimization Algorithm
An improved gene sequence optimization method, the systematic optimization method, is described for boosting the recombinant expression of genes in bacteria, yeast, insect and mammalian cells. This general method takes into account of multiple, preferably most or all, of the parameters and factors affecting protein expression including codon usage, tRNA usage, GC-content, ribosome binding sequences, promoter, 5′-UTR, ORF and 3′-UTR sequences of the genes to improve and optimize the gene sequences to boost the protein expression of the genes in bacteria, yeast, insect and mammalian cells. In particular, the invention relates to a system and a method for sequence optimization for improved recombinant protein expression using a particle swarm optimization algorithm. The improved systematic optimization method can be incorporated into a software for more efficient optimization.
Owner:NANJING GENSCRIPT BIOTECH CO LTD
Two-stage hybrid particle swarm optimization clustering method
InactiveCN102663100AImprove accuracyReduce computational complexitySpecial data processing applicationsData setPhacus
The invention relates to a two-stage hybrid particle swarm optimization clustering method, which is mainly used for solving the problems of greater time consumption and low accuracy of the conventional particle swarm optimization K-mean clustering method when the number of dimensions of samples is higher. The technical scheme disclosed by the invention comprises the following steps: (1) reading a data set and the number K of clusters; (2) taking statistics on information of dimensionality; (3) standardizing the dimensionality; (4) calculating a similarity matrix; (5) generating a candidate initial clustering center; (6) performing particle swarm K-mean partitional clustering; and (7) outputting a particle swarm optimal fitness value and a corresponding data set class cluster partition result. According to the two-stage hybrid particle swarm optimization clustering method disclosed by the invention, the first-stage clustering is firstly performed by adopting agglomerative hierarchical clustering, a simplified particle encoding way is provided, the second-stage clustering is performed on data by particle swarm optimization K-mean clustering, the advantages of hierarchical agglomeration, K-mean and particle swarm optimization methods are integrated, the clustering speed is accelerated, and the global convergence ability and the accuracy of the clustering result of the method are improved.
Owner:XIDIAN UNIV
Boiler combustion optimizing method
InactiveCN101498457AAvoid negative effectsImprove predictive performanceAdaptive controlProgramme total factory controlSupport vector machineRadial basis function neural
The invention relates to a method for optimizing combustion of a boiler. The combustion optimization of the prior boiler mainly depends on debugging stuffs to do experiments, thereby taking time and labor and obtaining limited parameter combinations. The method includes the following steps: collecting working parameters of the boiler and corresponding indexes characterizing the combustion characters of the boiler and building a real-time database; adopting an integrated modeling method supporting a vector machine to carry out modeling under the condition that the real work load is 60 percent smaller than the design load of the boiler and adopting a radial basis function neural network integrated modeling method to carry out modeling under the condition that the real work load is60 percent larger than or equal to the design load of the boiler to build boiler combustion models with different indexes; and utilizing the particle swarm optimization algorithm and combining with the built models to optimize the combustion parameter setting of the boiler according to different combustion indexes or index combinations of the boiler. The invention improves the predictive ability of the integral model, greatly improves the predictive ability of the models, and carries out one-line optimization and off-line optimization.
Owner:HANGZHOU DIANZI UNIV
Deep belief network feature extraction-based analogue circuit fault diagnosis method
ActiveUS20190243735A1Improve feature extraction performanceImprove the extraction effectDetecting faulty hardware using neural networksAnalog circuit testingRestricted Boltzmann machineFeature extraction
A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.
Owner:WUHAN UNIV
Planning method for distributed power source in power distribution network
ActiveCN104376410ALow costResourcesSystems intergating technologiesProbabilistic load flowAddress constant
The invention discloses a planning method for a distributed power source in a power distribution network. The planning method includes the steps that a distributed power source planning model in the power distribution network is established; according to the establishment of the distributed power source, multiple scenes are selected and time sequence features and probability features of the distributed power source are taken into consideration on the basis of analyzing the typical time sequence features of the distributed power source and analyzing the probability features of the distributed power source, and an indefinite model of the distributed power source is established; according to the load flow calculation of a power system, a probabilistic load flow calculation method based on a semi-invariant method is adopted for conducting the load flow calculation; the power distribution network accessing position and volume of the distributed power source are determined, wherein the probabilistic load flow calculation based on the semi-invariant method is embedded into the particle swarm optimization for solving the optimization problem, the method of a penalty function is used for processing constraint conditions, and the optimized optimal solution serves as the address constant volume scheme of the distributed power source. According to the planning method, the time sequence features and the randomness of the distributed power source can be involved at the same time, and the unit earning and cost, obtained after the access of the distributed power source, of the power distribution network are taken into consideration.
Owner:STATE GRID CORP OF CHINA +3
Electric-automobile-contained micro electric network multi-target optimization scheduling method
ActiveCN105160451ALow operating and maintenance costsImprove environmental benefitsResourcesState of artMicrogrid
Owner:上海顺翼能源科技有限公司
Improved particle filter-based mobile robot positioning method
ActiveCN103487047AAccurate pose estimationAccurate estimateNavigational calculation instrumentsMachine learningSimulationParticle swarm optimization
The invention provides an improved particle filter-based mobile robot positioning method. The improved particle filter-based mobile robot positioning method comprises the following steps: establishing a motion equation and a road sign calculation equation of a robot; optimizing a particle set by using a multi-agent particle swarm optimization algorithm, wherein the obtained optimal value is estimation of a pose; estimating an environmental road sign by using Kalman filtering algorithm; updating and normalizing the weight and resampling. The positioning method is accurate in positioning and easy to implement; the pose estimation and the environmental road sign estimation of the mobile robot are more accurate in a simulation process of the mobile robot.
Owner:DEEPBLUE ROBOTICS (SHANGHAI) CO LTD
Self-adaption stochastic resonance weak signal detecting method based on particle swarm optimization algorithm
InactiveCN102735330AEfficient detectionFast convergenceSubsonic/sonic/ultrasonic wave measurementBiological modelsSelf adaptiveStochastic resonance
The invention relates to a self-adaption stochastic resonance weak signal detecting method based on a particle swarm optimization algorithm. The method comprises the following steps of 1) particle swarm initialization; 2) step-changed stochastic resonance; 3) individual fitness evaluation; 4) particle speed and position updating; 5) termination condition judgment and 6) detection result output. The self-adaption stochastic resonance weak signal detecting method has the advantages that the simplicity is realized, the implementation is easy, the application range is wide, the convergence speed is high, high-frequency weak signals at high-noise background can be effectively detected, and a novel method is provided for stochastic resonance parameter self-adaption selection and practical application in engineering.
Owner:TIANJIN UNIV
Method for planning global path of robot under risk source environment
InactiveCN102129249AImprove robustnessProcessing speedTarget-seeking controlPerformance indexFuzzy membership function
The invention discloses a method for planning the global path of a robot under risk source environment, which aims at providing a method for planning the global path capable of ensuring the robot to quickly accomplish tasks in high efficiency under the risk source environment; the method comprises the following steps of: (1) detecting and determining the information of the work environment of therobot, wherein the information comprises the starting point and the target point of the robot, the position and the shape of an obstruction, and the position of a risk source; (2) building the mode for the work environment of the robot; (3) defining the length of the path and the risk degree as two performance indexes for evaluating the good and bad of the path, wherein the two performance indexes are two target functions of the path planning problem; (4) globally optimizing the two target functions defined in the step (3) by utilizing improved multi-target particle group optimal algorithm soas to obtain a group of Pareto optimal path collection; (5) adopting a fuzzy membership function to simulate the preference of the decision maker on the task, and selecting an approving eclectic solution from the Pareto optimal path collection as the final moving path of the robot.
Owner:CHINA UNIV OF MINING & TECH
Metaheuristic-guided trust-tech methods for global unconstrained optimization
A method determines a global optimal solution of a system defined by a plurality of nonlinear equations by applying a metaheuristic method to cluster a plurality of search instances into at least one group, selecting a center point and a plurality of top points from the search instances in each group and applying a local method, starting from the center point and top points for each group, to find a local optimal solution for each group in a tier-by-tier manner. Then a TRUST-TECH methodology is applied to each local optimal solution to find a set of tier-1 local optimal solutions, and the TRUST-TECH methodology is applied to each tier-1 local optimal solution to find a set of tier-2 local optimal solutions. A best solution is identified among all the local optimal solutions as the global optimal solution. The heuristic method can be a particle swarm optimization method or a genetic algorithm method.
Owner:BIGWOOD TECH
Self-adapting analog quadrature modulation disbalance compensation method and device
ActiveCN101272373AFast gain imbalanceImprove performanceBiological modelsMulti-frequency code systemsSequence signalQuadrature modulation
The invention discloses a self-adapting analog quadrature modulation unbalance compensating method and a device thereof. The device of the invention comprises a baseband signal module, a DPD module, a self-adapting analog quadrature modulation AQM compensator, a local oscillation (LO) and a RF transmission channel, and a feedback channel. The method of the invention comprises the steps that: an AQM compensation algorithm and a control unit are used for transmitting a training sequence signal to firstly judge whether a DAC is provided with quadrature modulation compensator QMC; then pre-distorted baseband si (t) and a sq (t) signal and baseband IB and a QB signal sent by a feedback loop are obtained, and then measured and compared; the particle swarm optimization (PSO) is applied to continuously adjusting the 6 compensating parameters until a target function reaches the global minimum; at last, a compensating parameter is applied to the QMC unit updated parameter to conduct a dynamic compensating process to the pre-distorted baseband si (t) and sq (t) signals, therefore, the AQM self-adapted unbalance compensation can be realized, and the performance of a broadband digital pre-distorted system is improved.
Owner:WUHAN HONGXIN TELECOMM TECH CO LTD
Space manipulator track planning method for minimizing base seat collision disturbance
InactiveCN104526695AAccurate pose captureReduce base attitude disturbanceProgramme-controlled manipulatorKinematics equationsEngineering
The invention discloses a space manipulator track planning method for minimizing base seat collision disturbance, and belongs to the technical field of manipulator control. The space manipulator track planning method for minimizing the base seat collision disturbance includes: deriving a manipulator base seat attitude disturbance equation on the basis of establishing a space manipulator kinematical equation and a dynamical equation; designing a comprehensive optimized operator on the premise of considering tail capture pose accuracy and joint displacement limiting of a space manipulator, and optimizing manipulator configuration in a null space so as to achieve minimization of the base seat disturbance caused by collision; finally, using a particle swarm optimization algorithm to achieve track planning before the collision of the space manipulator from an initial pose to an ideal pose. The space manipulator track planning method for minimizing the base seat collision disturbance solves problems in the track planning before the collision of the space manipulator, achieves a simple and pellucid control process, performs novel and practical design of the comprehensive optimized operator, can achieve the purpose of reducing the base seat pose disturbance caused by the collision to the utmost on the premise of guaranteeing the accurate tail capture pose of the manipulator and preventing joint angles from exceeding limits.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Hybrid particle swarm optimization algorithm in combination with genetic algorithm
InactiveCN108399451AEfficient optimization calculationForecastingArtificial lifeGenetic algorithmMetapopulation
The invention discloses a hybrid particle swarm optimization algorithm in combination with a genetic algorithm. The advantages of strong global search capability of a particle swarm optimization (PSO)algorithm and high local convergence speed of the genetic algorithm (GA) are integrated. Firstly, global search is performed by virtue of the characteristic of the strong global search capability ofthe PSO algorithm, and when iteration is performed for a specified algebra and is close to a globally optimal solution, at the moment, a whole population enters a neighborhood of the globally optimalsolution; and secondly, local quick search is performed in the neighborhood of the globally optimal solution by utilizing an improved genetic algorithm, so that the globally optimal solution is reached finally.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Method for identifying Hammerstein models
InactiveUS20110125685A1Readily apparentDigital computer detailsDigital dataRadial basis function neuralState model
The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part.
Owner:KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS
Design optimization method and optimization device of power assembly mounting system
The invention provides a design optimization method and an optimization device of a power assembly mounting system. The design optimization method comprises the steps of establishing a differential equation of a space six-freedom degree vibration model of the power assembly mounting system; analyzing to obtain an inherent frequency, an inherent vibration mode and vibration energy coupling among six freedom degrees according to inherent characteristics of the differential equation for the power assembly mounting system; establishing a multiple target optimization function of the power assembly mounting system according to each order inherent frequency, inherent vibration mode and vibration energy coupling; and carrying out optimization design with a particle swarm optimization algorithm. A dynamical model and the optimization function of the power assembly mounting system are established, the multiple target optimization function with reasonable distribution of mounting modal frequency and decoupling degree of energy as targets is determined, and a parallel optimization multiple target algorithm is subsequently adopted to obtain a multiple target optimization scheme set of the power assembly mounting system so that the designed power assembly mounting system can best meet the performance requirements of energy decoupling and modal distribution.
Owner:BAIC MOTOR CORP LTD
Multi-objective hybrid particle swam optimization design method for double-fed wind power generator
InactiveCN101833607AMeet performance requirementsIncrease cost benefitAC motor controlSpecial data processing applicationsSimulationWind power generation
The invention belongs to the field of motor optimization designs, and relates to a multi-objective hybrid particle swam optimization design method for a double-fed wind power generator. The method comprises the following steps of: (1) determining a constraint condition and a to-be-optimized design variable of the double-fed wind power generator, and establishing sub-objective function equations to form a multi-objective function; (2) constructing variable space by using the to-be-optimized design variable, and constructing non-dominated solution sets of population according to the quality of an objective value; (3) ordering the non-dominated solution sets according a Pareto domination mechanism, determining a population niche by taking a non-dominated solution as a core, establishing a particle speed updating mechanism and finally obtaining an optimal design scheme; manufacturing a mode machine according to the optimal design scheme, inspecting an actual operation index of a motor, and comparing the actual operation index with an index given by the design scheme; and if the actual operation index exceeds a requirement range of operation indexes, adjusting a performance design scheme. Due to the adoption of the motor optimization design method provided by the invention, overall economic benefit and wind energy utilization rate of a wind power generating system can be improved.
Owner:TIANJIN UNIV
Path planning method for mobile robots based on particle swarm optimization algorithm
InactiveCN101604166AEffectively foundVehicle position/course/altitude controlAdaptive controlDepth-first searchMobile robots path planning
The invention relates to a path planning method for mobile robots based on a particle swarm optimization algorithm. The prior path planning method for the mobile robots has single path selection and is easy to come to a dead end. The method comprises the concrete steps of: modeling the environment first; using a polygon to represent an obstacle; using a point to represent a robot, using a small square frame to represent the original position of the robot; and using a cross to represent a target point, wherein a starting point of the robot is marked as S, and the target point is marked as G; planning paths of the robot by utilizing the particle swarm optimization algorithm; and finally performing the deep first search on the planned paths. The method adds the deep first search into a polar coordinate particle swarm. By the method, the robot can effectively find one collision-free path in most of complex environments, and finally reach target points.
Owner:SERVICE CENT OF COMMLIZATION OF RES FINDINGS HAIAN COUNTY
On-line monitoring method of low-frequency oscillation of power system
InactiveCN102305891AHigh measurement accuracyHigh mode resolutionVoltage-current phase angleFrequency analysisBandpass filteringFiltration
The invention relates to an on-line monitoring method of low-frequency oscillation of a power system. In the on-line monitoring method, parallel filtration calculation is carried out at an oscillation frequency by creatively utilizing time-frequency atom compound band-pass filter function to obtain oscillation mode number, practical oscillation frequency distribution and real-time amplitude information. On the basis of the redundancy of adjacent real-time amplitude information, various oscillation mode amplitudes and decay time constants can be obtained by utilizing least square optimization estimation; based on the obtained oscillation frequency distribution, amplitude and decay time constant, a low-frequency oscillation signal model can be built, wherein only an initial phase and direct-current component amplitude of each mode are unknown; and optimization estimation is carried out on the model by utilizing a particle swarm optimization algorithm to obtain the initial phase and the direct-current component amplitude. The method has good noise robustness, can be used for accurately distinguishing compound oscillation models, is favorable for the strong non-linear mode analysis ofthe power system and is convenient for on-line monitoring and application.
Owner:WUHAN UNIV
Identification method of kinetic model of six-degree-of-freedom mechanical arm
InactiveCN107498562AEasy to identifyImprove accuracyProgramme-controlled manipulatorGenetic algorithmKinetic parameter estimation
The invention relates to an identification method of a kinetic model of a six-degree-of-freedom mechanical arm. The identification method comprises the steps of establishing a linear kinetic model of the mechanical arm according to joint friction by utilizing an improved Newton-Euler method first, then introducing a PSO (Particle Swarm Optimization) algorithm, establishing an algorithm for estimating unknown kinetic parameters on the basis of the improved PSO algorithm according to the concept of variation in a genetic algorithm at the same time, finally, using an UR (industrial robot) as an experimental subject, exciting joints of the industrial robot to move by designing an exciting track, and sampling joint movement parameters, so that the estimation of the kinetic parameters of the UR is realized, and the kinetic model is verified according to the moment prediction accuracy. Experiments verify the accuracy and effectiveness of the kinetic model, which is identified by the identification method, of the industrial robot.
Owner:ZHEJIANG UNIV OF TECH
Vehicle type identification method based on support vector machine and used for earth inductor
InactiveCN102779281AImprove the efficiency of fusion processingImprove learning effectRoad vehicles traffic controlCharacter and pattern recognitionSupport vector machineInductor
The invention relates to a vehicle type identification method based on a support vector machine and used for an earth inductor. The vehicle type identification method includes the following steps: vehicle type waveform data which require to be identified are collected by the earth inductor; a plurality of numeralization features are extracted from waveforms, effective data are screened out, and the features are normalized; multilayer feature selection is performed according to the extracted features, and an optimal feature combination is picked out; a vehicle type classification algorithm based on the clustering support vector machine is built, and parameters in a classification function are optimized by adopting a particle swarm optimization algorithm; a binary tree classification mode is built, classifiers on all classification nodes are trained, and a complete classification decision tree is built; and earth induction waveforms of a vehicle type to be identified are input to obtain identification results of the vehicle type. The vehicle type identification method builds a waveform feature extraction and selection mode, simultaneously adopts the classification algorithm based on the support vector machine and the particle swarm optimization algorithm, greatly improves machine learning efficiency, and enables a machine to identify vehicle types rapidly and accurately.
Owner:TONGJI UNIV
Bivariate nonlocal average filtering de-noising method for X-ray image
The invention provides a bivariate nonlocal average filtering de-noising method for an X-ray image. The method is characterized by comprising the following steps: 1) a selecting method of a fuzzy de-noising window; and 2) a bivariate fuzzy adaptive nonlocal average filtering algorithm. The method has the beneficial effects that in order to preferably remove the influence caused by the unknown quantum noise existing in an industrial X-ray scan image, the invention provides the bivariate nonlocal fuzzy adaptive non-linear average filtering de-noising method for the X-ray image, in the method, a quantum noise model which is hard to process is converted into a common white gaussian noise model, the size of a window of a filter is selected by virtue of fuzzy computation, and a relevant weight matrix enabling an error function to be minimum is searched. A particle swarm optimization filtering parameter is introduced in the method, so that the weight matrix can be locally rebuilt, the influence of the local relevancy on the sample data can be reduced, the algorithm convergence rate can be improved, and the de-noising speed and precision for the industrial X-ray scan image can be improved, so that the method is suitable for processing the X-ray scan image with an uncertain noise model.
Owner:YUN NAN ELECTRIC TEST & RES INST GRP CO LTD ELECTRIC INST +1
Method for planning routes of multi-unmanned aerial vehicles based on particle swarm optimization algorithm
InactiveCN105841702AImplement extensionsImprove convergence accuracyNavigational calculation instrumentsRouting modelMathematical model
The invention provides a method for planning the routes of multi-unmanned aerial vehicles based on a particle swarm optimization algorithm. The method comprises the following steps: establishing a three-dimensional map for planning space of the routes of the multi-unmanned aerial vehicles at first; then constructing multi-unmanned aerial vehicle route planning models under the three-dimensional map, wherein the models mainly comprise a barrier model, a route model, an unmanned aerial vehicle state model, a constraint model and a multi-unmanned aerial vehicle route planning mathematic model; and solving the problem of multi-unmanned aerial vehicle route planning under the three-dimensional map by using the particle swarm optimization algorithm. The method provided by the invention improves multi-unmanned aerial vehicle route planning capacity in a complex environment and provides technical support for air traffic management platforms for unmanned aerial vehicles, autonomous flight systems for multi-unmanned aerial vehicles, etc.
Owner:SAIDU TECH BEIJING CO LTD
Large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow
ActiveCN103840521AImprove securityImprove economyBatteries circuit arrangementsSecondary cells charging/dischargingTransformerEngineering
The invention discloses a large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow. The method comprises the steps that a power grid dispatching center enables each cell centralized management unit to be equivalent to a storage battery, the cell centralized management units are brought into unified dispatching of a power grid, an optimization model is built by using a smooth daily load curve, the minimum transmission loss, the minimum number of regulation times of on-load voltage regulating transformers and the highest user satisfaction degree as objective functions and by being restrained and regulated by power grid safe and stable operation behaviors, the charging capacity of each cell centralized management unit is obtained by using the improved particle swarm optimization algorithm, and each cell centralized management unit is used for formulating a charging and discharging plane for each vehicle according to different requirements of electric vehicles. By means of the large-scale electric vehicle optimized charging and discharging system and method, various problems caused because a large number of electric vehicles have access to the power grid to be charged and discharged can be solved, charging and discharging of the electric vehicles are completed in sequence, the requirement of users for electricity consumption can be met, and meanwhile operation safety and economical efficiency of the power grid are improved by making full use of the electric vehicles.
Owner:WUHAN UNIV
Wind, light and water-containing multi-source complementary micro-grid hybrid energy storage capacity optimal proportion method
ActiveCN104701871AAvoid conflicting issuesImprove general performanceEnergy storageAc network load balancingCapacitancePower Balance
The invention discloses a wind, light and water-containing multi-source complementary micro-grid hybrid energy storage capacity optimal proportion method. According to the method, an annual output power curve of wind power generation, photovoltaic power generation and hydroelectric generation is simulated according to the distribution condition of natural resources such as wind, light and water, an annual load curve of a micro-grid is combined, system cost and power fluctuation are used as target functions, accumulator capacity and super-capacitor capacity are used as optimization variables, and meanwhile constraint conditions such as power balance constraint, maximum instantaneous power constraint, power supply reliability constraint, super-capacitor charge and discharge current and voltage constraint and accumulator SOC (System On Chip) constraint are determined to establish a wind, light and water-containing micro-grid hybrid energy storage optimization configuration model; optimized solution of the target functions is performed by using a fuzzy decision-containing multi-target planning GA-PSO (Genetic Algorithm-Particle Swarm Optimization) algorithm to obtain the optimal proportion of the hybrid energy storage capacity. Compared with the conventional GA algorithm and PSO algorithm, the method has the advantages that the convergence rate is higher and the problem of mutual conflict of the target functions in the multi-target optimization algorithm is avoided better.
Owner:STATE GRID CORP OF CHINA +3
Power system short-term load probability forecasting method, device and system
InactiveCN109214605AReduce forecast errorForecastingResourcesPredictive methodsConjugate gradient method
The invention discloses a power system short-term load probability forecasting method, a device and a system. The short-term load probability density forecasting model of Gaussian process quantile regression is established by selecting an optimal input variable set affecting the load. Firstly, the importance score of input variables is given by stochastic forest algorithm, and the influence degreeof each input variable is sorted. Secondly, particle swarm optimization algorithm is used to search the super-parameters of the model to form the optimal Gaussian process quantile regression prediction model, avoiding the adverse effect of artificial experience setting initial parameters on the prediction performance of the model. The invention can avoid the shortcomings of manual experience selection, the load forecasting model established in the optimal input variable set has low error, which further reduces the forecasting error, and overcomes the problems that the common conjugate gradient method is easy to fall into the local optimal solution, the iterative number is difficult to determine, and the optimization performance is greatly affected by the initial value selection, so that the self-searching and group cognitive ability can be brought into full play.
Owner:ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
Combined positioning method for moving multi-station passive time difference and frequency difference
ActiveCN107526073ARealize high-precision positioningAchieving High-Precision EstimationUsing reradiationObservation matrixGlobal optimal
The invention discloses a combined positioning method for moving multi-station passive time difference and frequency difference, wherein the method belongs to the field of passive positioning technology. The method comprises the following steps of establishing a time different positioning model; establishing a frequency different positioning model; constructing a time difference and frequency difference observation matrix epsilicon1, and designing a fitness function; initiating a group and various parameters; evaluating the fitness function value of each particle; sequencing all particles; when the algorithm satisfies a terminating condition, outputting a current global optimal value; reconstructing the time difference and frequency difference matrix epsilicon2; obtaining a weighted least square solution theta2 and a covariance matrix cov(theta2); and calculating position and speed of a radiation source. The combined positioning method has advantages of performing optimal value solving on the fitness function which is obtained from the time difference and frequency difference observation matrix, combining a particle swarm optimization algorithm with a least square algorithm, and realizing high-precision target position on the condition of four base stations, and furthermore calculating speed information of the target. The combined positioning method can realize high-precision estimation to the position of the radiation source and is not limited by a station site layout. Furthermore relatively high positioning estimation precision is realized.
Owner:HARBIN ENG UNIV
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