13 results about "Gravitational search algorithm" patented technology

The invention provides an air source heat pump heating group regulation and control method and device oriented to power grid interaction, and relates to the technical field of air source heat pump heating group regulation and control, and the method comprises the steps: outputting a power grid regulation and control performance index of each air source heat pump heating group based on a trained gated circulation unit neural network; clustering each air source heat pump heating group on the basis, and determining a target clustering result corresponding to the optimal effectiveness evaluation index; according to an actual regulation and control capability index calculated according to the power grid regulation performance evaluation index, the actual regulation and control capability of each polymer is sequenced; determining a polymer participating in regulation and control and a regulation and control task based on the target power expected to be regulated and controlled by the power grid, the polymer power up and down regulation capability and the actual regulation and control capability, and allocating the regulation and control task according to a gravitational search algorithm so as to enable the difference value between the target power and the output power of the polymer participating in regulation and control to be minimum; the technical problems that in the prior art, regulation and control means are rough, model adaptability is poor, and response capacity cannot be managed in a layered mode are solved.

The invention discloses a single-target and multi-target flood interval forecasting method considering an interval fitting coefficient, and the method comprises the following steps: 1, obtaining historical runoff and rainfall data of a target research region, and carrying out the data quality inspection; step 2, screening LUBE interval forecast factors; 3, constructing an interval forecast evaluation index system; step 4, constructing an LUBE interval forecasting model based on a three-layer dual-output ANN neural network; step 5, establishing a single-target interval forecasting model based on a gravitational search algorithm GSA; step 6, establishing a multi-target interval forecasting model based on a non-dominated sorting genetic algorithm NSGA-III; step 7, determining and selecting a Pareto solution according to the preference of a decision maker, and determining model parameters to obtain a forecast result; according to the method, an interval forecast evaluation index system is enriched, and meanwhile, the method can be expanded to the single-target and multi-target research field.

A variable speed wind turbine maximum power capture method, system and application thereof, the method is based on active disturbance rejection control, RBF neural network and variable speed wind turbine maximum power capture method, including the following steps: establishing the equivalent model of variable speed wind turbine; using the second order linear active disturbance rejection control (LADRC) algorithm to construct the generator torque controller; using the gravity search algorithm to optimize the generator torque controller parameters and obtain the optimal parameter data set; based on the optimal parameter data set, the radial basis function neural network is trained, and the trained radial basis function neural output is suitable for the generator torque controller parameters under the current wind speed condition. Using the gravity search algorithm to optimize the generator torque controller parameters and obtain the optimal parameter data set; based on the optimal parameter data set, the radial basis function neural network is trained, and the trained radial basis function neural output is suitable for the generator torque controller parameters under the current wind speed condition.

The invention relates to the technical field of power system modeling and simulation, in particular to an intelligent equivalent modeling method for a wind power plant, electronic equipment and a storage medium, and the method comprises the steps: obtaining the dynamic response data of a plurality of doubly-fed fans in the wind power plant in a typical fault scene of a power grid; performing first-layer grouping on the doubly-fed fans according to whether a crowbar circuit is put into the doubly-fed fans or not; respectively extracting low-voltage ride-through fault response characteristics of the first type of input units and the second type of non-input units to serve as second-layer grouping indexes; obtaining an initial mass center of an MI-GSA-K-means algorithm by using a gravitational search algorithm; iteratively calculating DBI indexes corresponding to different clustering numbers, and selecting the clustering number corresponding to the minimum value of the DBI indexes as the optimal clustering number; and aggregating the grouped doubly-fed fans in the same group into an equivalent unit, and determining a wind power plant dynamic equivalent model based on LVRT-GSA dual-stage clustering. According to the method, the consistency of the equivalent model and the system fault process is improved, and the model precision can be effectively improved.

The invention discloses an electric power task allocation and optimization method and system, and belongs to the technical field of electric power Internet of Things, and the method comprises the steps: obtaining a calculation task generated by a business terminal; extracting a feature vector T of the calculation task through an edge access point or an edge node controller; periodically broadcasting the resource state vector S of each edge node, and quickly matching the feature vector T of the task with the resource state vector S of the peripheral available edge node based on the resource state of the edge node of fuzzy logic; modeling a feature vector T of the task and a resource state vector S of a peripheral available edge node into a dynamic system, and performing dynamic cooperative task allocation based on an improved gravitational search algorithm; and unloading the task to the target edge node for execution according to the distribution result. The method has the advantages that network and load changes are responded in real time, the multi-dimensional quality requirement of power business is accurately met, and the task allocation and optimization effect with the optimal global efficiency of the edge cluster is achieved.

The invention provides a transformer fault detection method and system based on machine learning, and relates to the technical field of transformer fault detection. Comprising the following steps: acquiring dissolved gas data in transformer oil, and constructing an original feature data set; performing data preprocessing and feature expansion on the original feature data set to generate an expanded feature data set; dividing the extended feature data set into a training set and a test set; constructing a fault diagnosis model based on a support vector machine according to the training set and the test set, and optimizing parameters of the support vector machine by adopting a gravitational search algorithm to obtain an optimized support vector machine model; and using the optimized support vector machine model to diagnose the fault type of the transformer to obtain a diagnosis prediction result. The method solves the problem that the traditional method in the prior art excessively depends on expert experience and a single machine learning algorithm, so that the accuracy of a transformer fault diagnosis prediction result is low.

The invention discloses an optical joint virtual energy storage model double-layer optimization scheduling method considering uncertainty, and the method considers the uncertainty of photovoltaic power generation, and builds a photovoltaic power generation GAN mathematical model through collecting historical data. Virtual energy storage is introduced, that is, electrochemical energy storage and virtual energy storage form an energy storage system, and double-layer optimization scheduling of a light storage combined system is realized by establishing a light storage combined double-layer optimization scheduling model. And finally, adding an improved strategy to the gravitational search algorithm, and solving the optimal configuration parameters of the optical storage operation model by using the improved gravitational search algorithm. The improved strategy is added on the basis of the gravitational search algorithm, the global search capability of the optimization algorithm is improved, the optimal configuration parameters of the optical storage model are obtained, and reference is conveniently provided for system operation.

This application discloses a transformer condition monitoring method, device, and electronic equipment, relating to the field of power equipment. It involves sequentially performing data normalization, outlier detection, and missing value processing on the acquired raw transformer data. The correlation between various features in the preprocessed data is analyzed, and the preprocessed data is divided according to the analysis results to obtain feature data characterizing transformer condition changes. A prediction model is invoked to extract and predict features from the feature data, obtaining feature prediction data for a preset future time period. Key feature data is extracted from the feature prediction data using a feature selection algorithm, and a fault identification model is invoked to perform fault identification processing on the key feature data. The fault identification model is obtained by optimizing an artificial neural network model or support vector machine based on a gravity search algorithm. By performing multiple data processing and filtering processes from data preprocessing to model prediction, the reliability and accuracy of subsequent model predictions are ensured.

The invention discloses a dam break omen method based on gravitational search and a double-branch converter, and relates to the technical field of dam break, and the method comprises the steps: carrying out the preprocessing of a pre-obtained multi-source remote sensing image data set; constructing an initial feature set, and performing feature subspace optimization on the initial feature set by adopting a gravitational search algorithm; constructing a double-branch converter model, inputting the optimal feature subset into the double-branch converter model, and embedding the optimal feature subset into a gravitation coefficient matrix; mapping the fragment-level abnormal heat map into dam body pixel-level, section-level and reservoir-level space grids; cutting the double-branch converter model, distributing an edge lightweight subnet model to an edge node, receiving a hierarchical confidence map, and uploading the hierarchical confidence map to a cloud node; reasoning is carried out at the cloud node, and dam break early warning is carried out. According to the method, cross-modal correlation modeling under time dynamic is realized, texture details of a local area and the displacement trend of the dam body can be distinguished more finely, and the detection capability of progressive micro-deformation of the dam body is improved.

The application discloses a kind of complex product function structure configuration equilibrium solving method considering parameter misalignment, comprising: the direct influence interval matrix between the construction product function structure attribute;Design consensus reaching process measures the judgment of expert group and establishes updating mechanism;Affine arithmetic is used instead of interval analysis to avoid interval dependency problem;The reliable weight of product function structure attribute is calculated by improving DEMATEL method;The product function structure configuration equilibrium multi-objective optimization model considering parameter misalignment disturbance is constructed;Three kinds of intelligent optimization algorithms, improved gravity search algorithm based on adaptive mechanism, improved hybrid gravity search and differential evolution algorithm and projection gradient descent algorithm, are designed to solve the model and obtain the optimal configuration solution of product function structure.The application can effectively handle the parameter misalignment problem caused by cognitive uncertainty, and improve the reliability and balance of product function structure configuration.

This invention proposes a cooperative search method for underwater unmanned underwater vehicles (UUVs) based on an improved gravity search algorithm, relating to the field of UUV search. The method includes: deploying multiple virtual particles within the search area to form a gravitational field; dynamically updating the mass and orientation preference states of each particle during the search process; calculating the basic gravitational force acting on each UUV and performing orientation-sensitive corrections to obtain the total attractive force; calculating the repulsive force between the UUVs and modulating the repulsive force to obtain the total repulsive force; synthesizing the total attractive and repulsive forces to obtain the total resultant force, resulting in a new course and velocity; updating the position of the UUVs based on the new course and velocity, detecting and avoiding search area boundaries and obstacles, and updating the state of the detected particles. This invention achieves efficient adaptive cooperative search of an underwater UUV swarm by constructing a gravitational field using virtual particles.

The application discloses a power task allocation and optimization method and system, and belongs to the technical field of power Internet of Things. The method comprises the following steps: obtaining a calculation task generated by a business terminal; extracting a feature vector T of the calculation task through an edge access point or an edge node controller; periodically broadcasting a resource state vector S of each edge node, and quickly matching the feature vector T of the task with the resource state vector S of a peripheral available edge node based on a fuzzy logic edge node resource state; modeling the feature vector T of the task and the resource state vector S of the peripheral available edge node as a dynamic system, and performing dynamic collaborative task allocation based on an improved gravity search algorithm; and unloading the task to a target edge node for execution according to the allocation result. The application has the effects of real-time response to network and load changes, accurate satisfaction of multi-dimensional quality requirements of power business, and realization of task allocation and optimization with the globally optimal efficiency of edge clusters.

The present application relates to the technical field of electric data processing, more particularly, the present application relates to a conference content context-aware question and answer retrieval method and system for continuous conferences, comprising: acquiring multi-session conference text data of continuous conferences and user input retrieval requirements; performing vectorization processing on the conference text data and the retrieval requirements to obtain conference content vectors and retrieval vectors; and calculating semantic similarity of each session conference and the retrieval requirements.The present application constructs theme consistency drift index and time-effect correlation saturation, accurately quantifies evolution and turning point of conference theme and actual value of historical information, and on this basis, uses context correction coefficient to improve the gravitational search algorithm, so that the search process can dynamically adjust the gravity according to the context correlation strength, thereby effectively excluding obsolete information and achieving accurate and time-effective knowledge retrieval in long-span and complex logic continuous projects.