Patents
Literature
Eureka-AI is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Eureka AI

1200 results about "Particle swarm optimization" patented technology

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.

Power distribution network double layer planning method considering the time sequence and the reliability

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

Two-stage hybrid particle swarm optimization clustering method

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

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

Electric-automobile-contained micro electric network multi-target optimization scheduling method

The invention relates to an electric-automobile-contained micro electric network multi-target optimization scheduling method. The method is characterized by comprising steps that, 1), a mode of access of an electric automobile to a micro electrical network is determined, discharging and charging load distribution characteristic superposition of a single electric automobile under different access modes is carried out to obtain discharging and charging load distribution characteristics of the electric automobile; (2), the electric automobile is taken as a micro electric network scheduling object which is added for electric network optimization scheduling, and an micro electric network scheduling model in consideration of large-scale electric automobile access is established according to the discharging and charging load distribution characteristics of the electric automobile; and 3), a particle swarm optimization algorithm based on the automatic recombination mechanism is employed to solve the micro electric network scheduling model in consideration of large-scale electric automobile access, economical efficiency of micro electric network scheduling under various scheduling strategies is compared and analyzed, and thereby the optimum scheduling strategy is obtained. Compared with the prior art, the method further has advantages of comprehensive consideration and effective and feasible performance.
Owner:上海顺翼能源科技有限公司

Space manipulator track planning method for minimizing base seat collision disturbance

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

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

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

On-line monitoring method of low-frequency oscillation of power system

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

Vehicle type identification method based on support vector machine and used for earth inductor

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

ActiveCN102609904AFast Noise CancellationProcessing speedImage enhancementPattern recognitionX-ray
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

Large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow

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

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 interval predication method based on nucleus limit learning machine model

The present invention belongs to the field of power prediction of wind power generation and particularly relates to a method for predicting a wind power interval based on a particle swarm optimization nucleus limit learning machine model. The method comprises: carrying out data preprocessing, i.e. preprocessing historical data in SCADA according to correlation between a wind speed and power; initializing a KELM model parameter and carrying out calculation to obtain an initial output weight betaint; initializing a particle swarm parameter; constructing an optimization criterion F according to an evaluation index and carrying out particle swarm optimization searching to obtain a model optimal output weight betabest; and bringing test data into a KELM model formed by betabest to obtain a wind power prediction interval and evaluating each index of the prediction interval. The method is easy for engineering realization; a good prediction result can be obtained; not only can a future wind power possible fluctuation range be described, but also reliability of the prediction interval is effectively evaluated, possible fluctuation intervals of wind power at different confidence levels are given out and reference is better provided for a power system decision maker.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)

Method for diagnosing and classifying faults of photovoltaic power generation arrays on basis of particle swarm optimization support vector machines

The invention relates to a method for diagnosing and classifying faults of photovoltaic power generation arrays on the basis of particle swarm optimization support vector machines. The method particularly includes steps of S1, acquiring a plurality of electric parameters of the photovoltaic power generation arrays to obtain electric parameter sample combinations when the photovoltaic power generation arrays work at the maximum power points; S2, normalizing each electric parameter sample; S3, acquiring test sample combinations according to normalized electric parameter sample combinations; S4, computing the optimal SVM (support vector machine) kernel function parameters g and penalty parameters c by the aid of PSO (particle swarm optimization) algorithms; S5, training the samples according to the optimal kernel function parameters g and the penalty parameters c to obtain training models; S6, detecting and classifying the faults of the photovoltaic power generation arrays by the aid of the training models. The method has the advantage that the photovoltaic power generation array fault detection and classification accuracy can be effectively improved by the aid of the method.
Owner:福建至善伏安智能科技有限公司

Method for optimizing demand-side time-of-use power price based on photovoltaic grid-connected uncertainty

The invention discloses a method for optimizing demand-side time-of-use power price based on photovoltaic grid-connected uncertainty. The method comprises the following steps: obtaining the daily load data after distributed energy is connected, and determining load peak-valley periods according to a fuzzy membership function; determining the objective function and the constraint condition of the optimization method of the demand-side peak-valley time-of-use power price in an operation cycle, and creating a peak-valley time-of-use power price optimizing model; by using a chance constraint theory, converting power balance constraint into deterministic equality constraint; and obtaining optimal peak-valley time-of-use power price by using a particle swarm optimization algorithm. The method provided by the present invention, based on peak shaving and load shifting adjustment of a power distribution network demand side according to the peak-valley time-of-use price optimization model, takes account of the connection of distributed power. At the same time, the optimization method adopts the chance constraint theory to solve the problem of prediction uncertainty of distributed power grid-connected power, reduces the decision risk caused by uncertainty and improves the pricing rationality of peak-valley time-of-use price.
Owner:YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products