459 results about "Swarm algorithms" patented technology

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention discloses a source-grid-load-storage integrated coordinated management and control system for an intelligent micro-grid, and relates to the technical field of intelligent micro-grids and comprehensive energy regulation and control. The system comprises the following components: a multi-energy-flow data acquisition unit, a multi-energy-flow coupling modeling and optimizing unit, a multi-energy-flow gradient utilization execution unit, a mode self-adaptive switching unit and a main control unit, according to the invention, through a multi-energy flow coupling modeling and optimization unit, an electric-thermal-gas multi-energy flow coupling model comprising a heat supply network transmission loss calculation sub-model is constructed, a renewable energy consumption constraint sub-model is additionally arranged, and an improved hybrid particle swarm optimization algorithm is adopted to carry out dynamic optimization solution, so that an optimal scheduling strategy is generated; according to the strategy, the consumption rate of renewable energy sources is increased, electricity-heat gradient utilization and efficient configuration are achieved through a heat energy distribution priority regulation and control mechanism and efficiency optimization control of the waste heat recovery module, and dependence of heat loads on electric energy is remarkably reduced.

The invention discloses an underwater gliding robot anchoring process energy consumption optimization method and system based on a subdomain grey box model. The energy consumption optimization method comprises the steps that a white box energy consumption mechanism model in the underwater gliding robot anchoring process is established; the whole anchoring energy consumption stage of the underwater gliding robot is divided into a plurality of sub-domains, and a Latin hypercube design method is adopted to simulate and generate a plurality of sub-domain samples meeting variable range constraints by using a hardware-in-the-loop simulation platform; selecting a Kriging model as a sub-domain agent model, fusing the white-box energy consumption mechanism model and constraint conditions of an oil bag volume and a movable mass position to form a sub-domain grey-box model, and performing segmented energy consumption fitting by using the sub-domain grey-box model; and with minimization of energy consumption fitted by the sub-domain grey box model as a target, performing iterative optimization on the sub-domain proxy model by adopting a dynamic guidance self-adjusting particle swarm algorithm, and outputting optimal planning parameters. The method realizes anchoring full-process low-energy-consumption control, and is suitable for marine resource exploration, hydrological monitoring and other tasks.

The invention provides an intelligent detection method and system for an urban sewer pipeline, and the method comprises the steps: synchronously collecting pipeline data through laser, vision, ultrasound and a pressure sensing array, and generating aligned multi-source data through space-time registration; edge end defect preliminary screening is realized by using a lightweight AI model, and high-risk data is screened and uploaded to a cloud end; a pipeline digital twin model is constructed based on point cloud splicing and feature recognition, a multi-modal fusion neural network is adopted to realize accurate defect diagnosis, and a detection path is dynamically optimized in combination with an ant colony algorithm. According to the method, the technical problems that the adaptability is poor and the omission ratio is high in a complex sewer pipeline environment and multi-source data fusion analysis and active early warning cannot be realized by a traditional method can be solved.

The invention relates to the field of motor structure design and optimization, and discloses a cogging torque and torque ripple optimization method for a permanent magnet motor for a vehicle. The method comprises the steps that based on an electromagnetic scheme, a geometric boundary and a term dictionary, a parameterized geometric prototype containing four symmetric semi-arc auxiliary grooves is constructed, and a design variable set structure is generated; converting the structure into a target evaluation interface structure containing a target item and a constraint item; an improved adaptive particle swarm algorithm is combined with finite element rapid check and a new iteration function to carry out optimization solution, and an updated candidate group structure is generated; and finally, optimal parameters are screened through Pareto sorting and robustness recheck, and an engineering configuration structure is generated. According to the method, through systematic parameter modeling, an efficient collaborative optimization algorithm and simulation evaluation, the optimization efficiency and reliability are remarkably improved, the cogging torque and the torque ripple of the motor are effectively reduced, and meanwhile, the process feasibility and the performance robustness of a design scheme are ensured.

The invention discloses a micro-grid group scheduling method and system based on hierarchical regulation and elastic load response, and belongs to the technical field of power system collaborative optimization and energy transaction, and the method comprises the steps: obtaining the power generation potential of a power generation side and the power utilization regulation potential of a demand side based on the operation constraint of the power generation side and the response constraint of the demand side; constructing a micro-grid group layered interactive coupling model based on the execution rule and the information interaction constraint; constructing a marketization transaction strategy model based on the transaction mode, the bidding mechanism and the income distribution mechanism; improving the particle swarm algorithm through model prediction control to obtain an optimized particle swarm algorithm; and based on the power generation of the power generation side, the power utilization regulation potential of the demand side, the micro-grid group interactive coupling model and the marketization transaction strategy model, obtaining an optimal scheduling scheme by using an optimized particle swarm algorithm. The technical problem that the feasibility of a scheduling scheme is difficult to improve in the prior art is solved.

The present application relates to the field of power system optimization. Disclosed are a multi-objective particle swarm optimization method and system based on multi-strategy improvement. The method comprises: using a multi-strategy improved multi-objective particle swarm optimization algorithm to solve a multi-objective optimization model of a power supply of a generator state monitoring apparatus; and combining three improved strategies, i.e., adaptive adjustment of an inertia weight, coexistence of a decomposition algorithm and Pareto dominance, and introduction of a mutation factor. The present application overcomes the defects of conventional multi-objective particle swarm algorithms, and achieves a better distribution of a Pareto front, thereby obtaining the best Pareto optimal solution set. The present application solves the problems of conventional multi-objective particle swarm algorithms in solving a multi-objective optimization problem, such as premature convergence to a local non-dominated solution, and sub-optimal distribution of a Pareto front caused by an improper external archive update strategy, thereby improving the operational efficiency and reliability of a power supply system of an apparatus.

The invention relates to a hydrogen production PEM electrolytic cell array power distribution method and system. The method comprises the following steps: establishing a photovoltaic power generation power and wind power model; establishing an initial database according to historical and real-time natural condition data; preprocessing the data in the initial database, and dividing the data into a training set, a test set and a verification set in proportion; training a random forest algorithm by using the training set to obtain final prediction power; performing differential accumulation according to the fluctuation change of the final predicted power to calculate a rolling period; defining a control state of the electrolytic cell; calculating the electrolytic cell health degree of each electrolytic cell; establishing a power-health degree adaptability function; solving the adaptability function by using a particle swarm algorithm to obtain a control state and preset power; and in the current rolling period, operating the electrolytic cell system according to the control state and the preset power of each electrolytic cell calculated by the adaptability function in the previous rolling period. The invention optimizes the system efficiency and the life of the PEM electrolytic cell.

The invention discloses a grid-connected three-phase LCL inverter parameter monitoring method for digital twinning modeling. The method comprises the following steps: acquiring actual measurement data of a grid-connected three-phase LCL inverter; and according to actual measurement data, performing iterative optimization on monitoring parameter sets of a digital twin model of the grid-connected three-phase LCL inverter based on an adaptive particle swarm algorithm, including: operating the digital twin model under each monitoring parameter set to obtain corresponding multiple groups of model output data, and calculating a corresponding error objective function, taking the monitoring parameter set corresponding to the minimum target function value as an optimal monitoring parameter set; if an error objective function value responding to the optimal monitoring parameter set is smaller than a function threshold value, outputting the error objective function value as a final optimal monitoring parameter set, and performing subsequent parameter monitoring; and otherwise, iteration optimization is continued. And high-precision calibration of the digital twin model and the real inverter is realized, so that key parameters which are difficult to directly measure or high in measurement cost in the real inverter are calculated in real time.

Disclosed in the present application are a DAB converter control method based on global minimum backflow power, a device, and a storage medium. The method comprises: acquiring the current consumed power inside a DAB converter, and determining whether the current consumed power is equal to a preset power threshold; if the current consumed power is not equal to the preset power threshold, acquiring the current input voltage and the current output voltage of the DAB converter, and then, with the objective of minimizing the deviation of the average transmitted power of the DAB converter and the output backflow power of the DAB converter, using a particle swarm optimization algorithm to optimize the current phase shift ratio combination applied by the DAB converter during triple phase shift control, so as to obtain an optimal phase shift ratio combination; and on the basis of a preset voltage reference value and the current output voltage of the DAB converter, determining an outer phase shift angle correction value, and then controlling the DAB converter by combining the outer phase shift angle correction value with the optimal phase shift ratio combination. The present application introduces the particle swarm optimization algorithm for solving a multi-objective optimization problem, such that the DAB converter can maintain a relatively high energy transmission efficiency and reduce the effect of the output backflow power after operation adjustment.

The invention relates to the technical field of control system data processing, in particular to a ship intelligent workshop crane hoisting path planning method, which comprises the following steps: collecting crane coordinates, obstacle distribution, load swing angle and material weight, and inputting a physical constraint model to generate a structured environment state data set; initializing wolf pack algorithm parameters based on the structured environment state data set, calculating a compressed detection wolf walking step length according to an obstacle distance, generating a global task queue by an industrial cloud platform according to a dynamic parameter set, and outputting an execution state data set by a crane end; counting emergency braking times from an execution state data set to optimize a walk step length function, comparing task time to optimize a wolf investigation proportion, analyzing historical swing data to establish a step length and swing mapping library, and optimizing a parameter set to feed back and update a physical constraint model. According to the invention, cooperation of environment perception, dynamic path planning and closed-loop verification is realized, and accident risks and production delay in a ship workshop narrow space dynamic obstacle scene are reduced.

The invention relates to an excavator trajectory planning method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of engineering machinery trajectory planning. The method comprises the following steps: establishing a forward kinematics model through a D-H homogeneous coordinate transformation method; based on the forward kinematics model, establishing an inverse kinematics model through a geometric method; setting six path points in the track according to the mining track; based on an inverse kinematics model, converting the space coordinates of the six path points into space angles of joints; interpolation is carried out on the joint path points through a five-time B spline curve, and a track is generated; determining an optimization objective function according to actual application requirements, and setting constraint conditions according to a specific excavator type; and finally, the optimization problem is solved through an IADEMOPSO algorithm, an optimal Pareto solution is selected by using an average optimal evaluation formula, and an optimal track is generated. The track planning of the excavator can be realized.

The invention discloses a multi-path planning method and device, and relates to the technical field of automatic driving. A specific embodiment of the method comprises the following steps: acquiring a starting point and an ending point of a to-be-planned path; initializing a particle swarm based on the starting point and the ending point of the to-be-planned path; each particle in the particle swarm corresponds to a candidate path; based on a simulated annealing particle swarm algorithm and the target function of the round, determining a planned path of the round from the candidate paths; wherein the target function of the current round is obtained according to a sequential ecological niche algorithm and the target function of the previous round. According to the embodiment, the simulated annealing particle swarm optimization and the sequential niche algorithm are combined to realize multi-path planning, the population can be effectively prevented from falling into a local optimal solution, the target function is updated through sequence optimization, the search of all or local optimal paths is ensured, and the efficiency and accuracy of path planning are improved.

The unmanned aerial vehicle path planning method based on chaos adaptive particle swarm optimization comprises the following steps: constructing a three-dimensional task scene model, setting flight performance constraint conditions, and constructing a flight path fitness objective function; initializing particle swarm parameters and population positions; calculating an adaptive value of each particle, and updating an individual historical optimal adaptive value / path and a group global optimal adaptive value / path; updating the particle speed and position; updating a current weight parameter based on a dynamic parameter self-adaptive decision-making mechanism; on the basis of a conditional trigger type population variation strategy, the positions of the particles are updated until the number of iterations reaches the maximum value, and an optimal path is output; according to the method, the optimal path is planned based on the chaos adaptive particle swarm algorithm by constructing the flight path fitness objective function, the initial solution of the algorithm is better, environment-population dual perception is realized, the convergence speed of path planning is improved, global exploration and local development are better balanced, and the method has better randomness and robustness.

The invention discloses a hub motor vector control method based on an improved grey wolf particle swarm algorithm, and the method comprises the steps: building a mathematical model of a hub permanent magnet synchronous motor, building a double-closed-loop vector control simulation model containing a speed loop and a current loop in Simulink based on the mathematical model, and selecting a speed loop PID controller as a to-be-set object; improving the original grey wolf particle swarm algorithm to obtain an improved grey wolf particle swarm hybrid algorithm; and performing iterative optimization on the three parameters Kp, Ki and Kd of the speed loop PID controller through the improved grey wolf particle swarm hybrid algorithm, and outputting the finally obtained global optimal solution q as the optimal values of the three parameters Kp, Ki and Kd of the speed loop PID controller to complete the hub permanent magnet synchronous motor vector control.

The invention provides an APU characteristic correction method and device, and belongs to the field of aviation electromechanics. The method provided by the invention comprises the following steps: establishing an APU system simulation model; determining a plurality of correction parameters and correction targets; calculating a calculation value corresponding to the correction target based on the APU system simulation model and the correction parameter; constructing a correction error function based on the calculated value of the correction target and a test value of the correction target obtained through a test; determining an updated component in the APU, and determining an optimization strategy based on the identity of the updated component; based on the optimization strategy, executing a particle swarm algorithm to obtain an optimal correction parameter corresponding to the correction error function; and updating the APU system simulation model based on the optimal correction parameter to obtain a corrected APU characteristic model. According to the APU characteristic correction method and device provided by the invention, the accuracy of the model correction result can be improved after the local parts of the APU are updated.

The invention discloses an efficient refrigerating machine room energy-saving automatic control system based on reinforcement learning, and the system comprises the following modules: an operation data collection module which is used for collecting key equipment operation parameters and generating unified time sequence data; the energy consumption modeling module constructs energy consumption mathematical models of the water chilling unit, a water pump and a cooling tower and stores the energy consumption mathematical models into a database; the cooling capacity prediction module is used for predicting the future cooling capacity demand based on the historical data and the environmental parameters; the feed-forward optimization module is used for optimizing power distribution by using an adaptive particle swarm algorithm and generating control parameters; the state correction module automatically adjusts the running state according to the running efficiency deviation condition; the cooling strategy optimization module introduces an improved DSAC algorithm to optimize a cooling control strategy; and the instruction deployment module deploys the strategy to an actual system and outputs a regulation and control instruction. The energy efficiency management level of the refrigerating machine room is improved, and integration of energy conservation and intelligent control is achieved.

The invention provides a fault repair and task unloading optimization method under a space-air-ground fusion vehicle-mounted network framework, and belongs to the technical field of Internet of Vehicles. According to the first stage, the optimal deployment point location of the unmanned aerial vehicle is searched globally based on the wolf pack algorithm, the flight path is planned dynamically in combination with the near-end strategy optimization algorithm, and network connectivity repair of a fault area is achieved; and in the second stage, a task unloading decision initial population is generated through multiple deep reinforcement learning agents, and a Pareto optimal solution set is obtained through optimization of a non-dominated sorting genetic algorithm II, so that multi-target balance of energy consumption, cost and time delay is realized. According to the method, the fault self-healing capability and task processing efficiency of the SAGVN can be remarkably improved, and the method adapts to the high-dynamic and high-reliability requirements of intelligent traffic.

The invention relates to the technical field of electric vehicle charging control, discloses an electric vehicle intelligent charging and discharging method and system based on robust optimization, and aims to solve the problems that a traditional deterministic optimization method is difficult to deal with photovoltaic output, load requirements and electricity price fluctuation and does not consider the service life of a battery and economical efficiency. The method comprises the following steps of: establishing a deterministic charging optimization model which takes minimization of power grid electricity purchasing cost and battery depreciation cost as a target and comprises constraints such as battery charge state dynamics and an operation mode; on the basis of the information gap decision theory robust optimization model, load, photovoltaic and electricity price robust boundaries are defined, and a worst scene is constructed; solving to obtain a Pareto optimal solution set by using a multi-target particle swarm algorithm; and selecting the solution with the maximum minimum satisfaction degree as a final strategy through the fuzzy satisfaction degree. Economical, reliable and battery-friendly charging and discharging scheduling can be realized in an uncertain environment, the method is adaptive to an intelligent micro-grid and a V2G scene, and the comprehensive operation performance is improved.

The invention belongs to the technical field of traffic flow green wave control, and particularly relates to a green wave control method based on traffic flow prediction driving. The method comprises the following steps: acquiring multi-dimensional real-time data in a distributed manner; performing abnormal value detection and data interpolation based on space-time neighborhood analysis to generate high-quality time sequence traffic flow data; modeling a road network into a dynamic graph fusing static connectivity and real-time traffic flow coupling degree, and constructing a double-time-scale time sequence diagram sequence; inputting an improved space-time diagram neural network, extracting time features by optimizing Bi-GRU, capturing spatial association by a multi-head attention mechanism, and outputting an accurate road section-level traffic flow prediction result through residual fusion; and finally, constructing a multi-objective optimization model based on prediction, solving by using an improved multi-objective particle swarm algorithm, obtaining a signal timing scheme, and issuing and executing the signal timing scheme. The method can dynamically adapt to traffic changes, improves the traffic efficiency, and avoids green wave failure and congestion.

The invention provides a collaborative model prediction control method for a regional cooling system penetrating through a source network load full chain, and belongs to the technical field of intelligent control of cooling systems. Collecting and preprocessing meteorological and operation characteristic data of the system; inputting the data into an Attention-LSTM cold load prediction model, determining an optimal step length in combination with thermal inertia analysis, and outputting a cold load prediction sequence; based on the unified energy path theory, a pipe network quasi-steady-state hydraulic and thermal coupling mechanism model is constructed; by taking load prediction as input and the coupling model as constraint, establishing an optimization model containing three targets of pump consumption power and the like; rolling optimization is carried out by adopting a multi-target particle swarm optimization algorithm, and optimal operation parameters meeting thermal unbalance degree constraints are obtained; and in combination with the peak-valley electricity price, a cold source unit and cold storage device collaborative scheduling strategy is formulated, and a final control instruction is generated. The phenomena of insufficient cold supply at the tail end and local supercooling caused by hydraulic imbalance of a pipe network and large hysteresis characteristics are effectively solved, and optimal control over the operation cost is achieved.

The invention relates to the technical field of diaphragm pump aging test, and discloses a two-parameter intelligent closed-loop diaphragm pump aging test system and method. According to the method, a temperature spectrum and a vibration spectrum are obtained through fast Fourier transform, a frequency domain coherence coefficient is calculated to identify temperature-vibration coupling characteristics, a resonance frequency band and a drift trend thereof are identified in real time by adopting a spectrum peak tracking algorithm, and a multi-objective optimization function considering aging acceleration efficiency and resonance risk is constructed. And solving the optimal temperature stress amplitude and vibration stress frequency by using a particle swarm algorithm, generating a stress loading instruction avoiding a resonance area through a self-adaptive PID controller, and dynamically updating model parameters based on a closed-loop feedback mechanism. According to the invention, the problem of resonance effect amplification caused by traditional fixed frequency stress loading is solved, and refined adaptive stress control is realized.

The invention provides a passive device parameter design method suitable for suppressing multi-mode broadband oscillation of a double-high power distribution network, and relates to the technical field of power distribution network electric energy quality treatment. According to the method, port impedance modeling is performed on the power distribution network, a node admittance matrix is established, and a key node for oscillation suppression is determined in combination with modal analysis; port admittance parameters of the passive device are equivalently injected into the node admittance matrix in parallel at the key node, iterative optimization is performed on the port admittance parameters by using a particle swarm algorithm, and a passive device parameter fitting result is obtained by taking a damping coefficient greater than or equal to zero point 2 and a resonance angular frequency satisfying an integral multiple of a 2-hundred product circumference rate as an optimization target; the resonance peak weakening effect is verified by constructing a frequency response model before and after the passive device is accessed. According to the method, systematized and collaborative suppression of multi-mode broadband oscillation is realized, and the stability of the double-high power distribution network is remarkably improved.

The invention relates to the technical field of intelligent inspection and cooperative control, and particularly discloses a robot dog and unmanned aerial vehicle cooperative inspection system and method based on an artificial bee colony algorithm, and the system comprises an unmanned aerial vehicle, a robot dog and a cooperative decision system. The robot dog comprises a navigation module, an environment sensing module, a first communication module and a charging platform; the unmanned aerial vehicle comprises a flight control module, a visual identification module, an image acquisition module, an energy management module and a second communication module; the collaborative decision module is used for operating an artificial bee colony algorithm to realize intelligent collaboration of the robot dog and the unmanned aerial vehicle; according to the invention, through dynamic conversion of roles, active exploration of an unknown area and autonomous precise charging on the mobile platform, all-weather, self-organization and full coverage of the inspection process are realized, and the inspection efficiency and the intelligent level in a complex environment are significantly improved.

The invention provides an unmanned aerial vehicle cluster weapon target collaborative allocation method and system under space-time constraint, and the method comprises the steps: defining a collaborative allocation object, constructing a collaborative combat scene target function, defining a collaborative combat scene constraint condition, and finally solving the target function through employing a multi-gene population parallel ant colony algorithm, encoding the weapon set, the target set and the unmanned aerial vehicle set into a weapon gene sequence, a target gene sequence and an unmanned aerial vehicle gene sequence respectively to generate an initial population, and performing staged joint optimization by fusing a pheromone guiding mechanism of an ant colony algorithm and crossover mutation operation of a genetic algorithm, the optimal cooperative allocation scheme is searched when the constraint condition is satisfied, the unmanned aerial vehicle task allocation result can be better obtained and optimized through the cooperative allocation mode, and the unmanned aerial vehicle cooperative allocation efficiency is improved.

Provided in the present invention is a multi-wind turbine coordinated yaw optimization method for an offshore wind farm. The method comprises: on the basis of a given wind speed, a given wind direction and given wind turbine parameters, establishing a single-wind turbine double-Gaussian yaw wake model and a multi-wind turbine yaw wake adaptive combined superposition model, and setting the overall operating conditions of a wind farm; on the basis of the overall operating conditions, setting relevant parameters of a particle swarm optimization algorithm, and by means of the particle swarm optimization algorithm with a penalty factor inserted, executing an iterative loop on a wind turbine yaw scheme; and outputting the wind turbine yaw scheme that meets a convergence condition, and using same as a multi-wind turbine coordinated yaw optimization scheme. In the present invention, a yaw multi-wind turbine wake adaptive combined superposition model is proposed on the basis of a yawed wind turbine double-Gaussian wake model with better accuracy and stronger robustness; global coordinated optimization is performed on wind turbine yaw by using a particle swarm optimization algorithm; and by means of introducing the penalty factor, non-compliant yaw strategies are identified and eliminated, thereby ensuring the smoothness of wind turbine operation and improving the power generation efficiency.

The invention discloses a power distribution network fault positioning method and device based on a quantum ant colony and a PSO algorithm, and belongs to the technical field of power system fault positioning. The method comprises the steps of obtaining topological structure data and real-time operation data of a power distribution network, and performing preprocessing; parameters of the quantum ant colony algorithm are initialized, path selection is carried out based on the quantum bit state transition probability, global search is carried out on the whole power distribution network, and a suspected fault area is obtained; and taking the suspected fault region obtained by the quantum ant colony algorithm as a search space of a PSO algorithm, initializing PSO algorithm parameters, performing local fine search through particle position and speed updating by using the PSO algorithm, and determining a fault position. Through organic fusion and cooperative work of the two algorithms, the global search capability of the quantum ant colony algorithm and the local search advantage of the PSO algorithm are fully played, the accuracy and efficiency of power distribution network fault positioning are effectively improved, and the adaptability of the algorithms to a complex topological structure is enhanced.

The invention discloses a UAV positioning and joint deployment and resource allocation method based on a radar, and relates to the technical field of UAV communication, the angle of a UAV relative to the radar is measured by deploying an EMVS-MIMO radar, the distance is determined by combining pulse ranging, and the three-dimensional position of the UAV can be estimated; according to the method, UAV deployment and GU association are optimized based on a BCD method, firstly, rapid convergence of a particle swarm algorithm and global optimization capacity of a grid search algorithm are combined, and UAV deployment is optimized; then, recalculating the GU association according to the new UAV deployment; a constrained particle swarm optimization algorithm is adopted to optimize the UAV transmitting power, and a variable learning rate is introduced to dynamically adjust the inertia weight, so that particles are helped to find a globally optimal solution; based on different requirements of communication and positioning GU on the bandwidth, a greedy algorithm is adopted to allocate the bandwidth of the UAV, and the fairness between the GUs is ensured.

The invention provides a double-layer optimization scheduling method for source network load storage collaborative loss reduction, and belongs to the technical field of source network load storage collaborative scheduling. A double-layer optimization framework comprising an upper layer planning model and a lower layer operation model is constructed, and a key node set is screened by using a node importance comprehensive scoring method based on a graph theory; the improved wolf pack algorithm is adopted to solve a double-layer coupling problem, a lower-layer operation effect is fed back to an upper layer to serve as a fitness evaluation basis, and in cooperation with population aggregation degree monitoring, a wandering wolf random jumping mechanism and a reverse learning strategy, deep collaboration and global optimal solution of a planning layer and an operation layer are achieved. The technical problem of poor network loss optimization effect caused by lack of an effective double-layer coupling solution mechanism for collaborative optimization planning and operation scheduling of the distributed photovoltaic and energy storage system in the power distribution network is solved.

The invention discloses a power distribution network voltage sag monitoring point multi-target selection method considering a distributed power supply low voltage ride through characteristic. The method comprises the following steps: providing a distributed power supply low voltage ride through strategy; deriving a voltage sag amplitude analytical expression under various short circuit fault types considering the low voltage ride through characteristic of the distributed power supply; introducing a position variable to construct a function relation between a voltage sag amplitude and a fault position; solving a voltage sag domain critical point by using a differential evolution algorithm; based on the sag domain critical point, establishing a monitoring point multi-target selection model taking economical efficiency, redundancy and construction urgency minimization as a target function; solving the established monitoring point multi-target selection model by adopting a multi-target particle swarm algorithm to obtain a Pareto optimal solution set; and according to the obtained Pareto optimal solution set, adopting an ideal point method to screen out an optimal point distribution scheme. According to the method, the voltage sag domain can be accurately divided, and multi-objective optimization of economical efficiency, low redundancy and low construction urgency of monitoring point layout is realized on the premise of satisfying panoramic observability.