37 results about "Mixed integer quadratic programming" patented technology

The invention discloses a parallel hybrid electric vehicle energy management method with gearbox gear control, and relates to the field of hybrid electric vehicle energy management. The parallel hybrid electric vehicle energy management method analyzes an evolution law of a state of charge change rate of a battery and engine specific fuel consumption under each gear, combines with a hybrid logic dynamic modeling method, establishes a parallel hybrid electric vehicle energy management system model including gearbox discrete gear information, further introduces a model predictive control idea, defines motor torque as a continuous control variable, and defines gearbox gears as discrete control variables and translates an optimization problem into a mixed integer quadratic programming problemto solve, the strategy optimizes the torque distribution of each power source and decides an optimal gearbox gear. The parallel hybrid electric vehicle energy management method aims at the energy management problem of a parallel hybrid electric vehicle with a gearbox, and has the advantages of high pertinence, high practicability, good optimization effect, high integration and the like.

The invention provides a linearized power flow-based network reconstruction method for a power distribution network and belongs to the technical field of operation control of power systems. The methodcomprises the steps of firstly building a network reconstruction model, formed by an objective function and constraint conditions, of the power distribution network; transforming the objective function and the constraint conditions separately and transforming an original model into a mixed integer quadratic programming model; and solving the transformed model to obtain open and closed state variables of various branches and carrying out corresponding switching actions according to the solving result to achieve network reconstruction. The linearized power flow-based network reconstruction method is high in computation speed and good in convergence and is suitable for being applied to the scenes of real-time network reconstruction of the power distribution network.

The invention relates to an intelligent soft-switching voltage control method for an active power distribution network based on chance-constrained programming. According to the technical features, theintelligent soft-switching voltage control method comprises the following steps that step 1, branch parameters are separately input according to a selected power distribution system; step 2, a k-means clustering algorithm is used for cluster analysis, and typical scenes for describing the uncertainty of distributed power output and the probability of occurrence of each scene are obtained; step 3,an intelligent soft switching voltage control model of the active power distribution network based on chance-constrained programming is established; step 4, auxiliary variables are introduced for quadratic programming model transformation; step 5, a mixed integer quadratic programming algorithm is used for solving; and step 6, the results of the step 5 are output. The intelligent soft-switching voltage control method solves the problem of intelligent soft switching voltage control strategies under different confidence levels, significantly reduces the number of voltage out-of-limit scenes, and comprehensively improves the operation level of the power distribution network.

The invention discloses a weight-based hybrid integer quadratic programming multi-target scheduling method. According to the method, coal consumption, network loss, emission and electricity purchasingcost are taken as multiple reference targets for scheduling, from the perspective of conversion, according to the weight of the different targets, a multi-target problem is converted into a single-target problem for solution, and thereby solution complexity of an original multi-target optimization model is reduced, and stability and robustness of the single-target solution algorithm can be utilized to the large extent.

The invention relates to an industrial process fault diagnosis method based on the Bayesian information criterion. The method comprises: collecting normal industrial data and calculating several kindsof detection statistics amounts based on normal data; carrying out fault detection on a to-be-detected sample; expressing a fault isolating task into a combinatorial optimization problem; convertingthe problem into a mixed integer nonlinear programming problem by combining the Bayesian information criterion; on the basis of a forward selection algorithm, simplifying the problem into a mixed integer quadratic programming problem; on the basis of a branch-and-bound algorithm, solving a series of similar mixed integer quadratic programming problem to obtain a fault variable combination causingthe sample fault. The industrial process fault diagnosis method has high universality; and the fault variable can be identified without predetermining a fault direction or a known historical fault data set. When the amplitude of the fault is small, an accurate diagnosis result is obtained. Besides, the combination optimization problem is transformed into the quadratic programming problem with sparse constraints for calculation, so that the computational efficiency is improved substantially.

The invention discloses a dynamic scheduling method of a mixed-integer quadratic programming power system. The dynamic scheduling method comprises the steps that a dynamic scheduling mathematic model of the power system is built, wherein the model comprises an objective function and conditional constraints; the conditional constraints include a power balance constraint, a unit output constraint, a unit ramping constraint, a unit embargo area constraint and positive and negative standby constraints; the conditional constraints are converted into corresponding linear constraints by employing a mixed-integer encoding technology to convert the dynamic scheduling mathematic model into a mixed-integer quadratic programming model; and the mixed-integer quadratic programming model is solved by employing a default engineering optimization software package to obtain a corresponding power system scheduling scheme. Therefore, firstly, the feasibility of the finally obtained power system scheduling scheme and the dynamic scheduling effectiveness of the power system are ensured; and secondly, a global optimal solution, namely the optimal power system scheduling scheme can be obtained, and the dynamic scheduling method is suitable for large-scale calculation of an active scheduling plan of a unit.

The invention relates to a mixed height unit legalization method for minimizing average and maximum movements. The method comprises the steps of representing a circuit as a hypergraph model; after directions are set for units, aligning the units to a power supply rail line which is adjacent and correctly matched; preprocessing the multi-line high-standard unit, and by analyzing and reconstructingan objective function and a constraint, planning a mixed height standard unit legalization model as a mixed integer quadratic programming model; converting the mixed integer quadratic programming model into a quadratic programming model; converting the quadratic programming model into a corresponding linear complementary model; solving the linear complementary model by using a matrix splitting iteration method based on modulus; carrying out line redistribution and restoring the multi-line high-standard unit; and legalizing the minimum movement unit by adopting a linear programming-based methodand a Kuhn-Munkres algorithm. According to the method provided by the invention, all the units are optimized at the same time, so that the requirements of a mixed height standard unit legalization stage of current VLSI can be met.

The invention is applicable to the technical field of intelligent network connection management and control, and provides a vehicle-road cooperative bus control method and device and terminal equipment. The method comprises the steps of acquiring traffic information of a target road network in a vehicle-road cooperative environment; determining whether a target bus exists in the target road network or not according to the traffic information; dividing the target road network into at least one sub road network by taking the bus station as a node; when the target bus exists in the target road network, calling a mixed integer quadratic programming model corresponding to the sub-road network where the target bus is located, carrying out optimization solution, and sending the optimized drivingspeed and parking time of the target bus to a vehicle-mounted terminal of the target bus, and sending the optimized timing scheme to traffic lights to adjust the time headway of the target bus, so that the regularity of arrival time of the same bus line can be effectively improved, the waiting time of passengers is stable, the passenger capacity of each bus is balanced, the overall efficiency of bus operation is improved at the same time, and road space-time resources are saved.

The invention, which belongs to the technical field of automatic scheduling automation field of the power system, especially relates to a robust state estimation method in a mixed integer quadratic programming mode. The method comprises: a voltage vector a node i is expressed in a right-angle coordinate mode and an injected active power and an injected reactive power of the node i are obtained; three equivalent two-winding transformers are used for expressing a three-winding transformer and all branch circuits in the power system network are expressed by using a unified pi type branch circuit; according to an equivalence circuit graph of the pi type branch circuit, branch circuit active powers and reactive powers from the node i to a node j as well as a voltage amplitude measurement equation of the node i are obtained, and a robust state estimation model in a mixed integer quadratic programming mode is also obtained; an initial value of an MIQCP robust state estimation model is obtained by using a WLS state estimation method; the MIQCP robust state estimation model is resolved and a defective measurement value is found and eliminated; and the corrected measurement value is calculated again by using the WLS state estimation method.

The invention discloses a comprehensive energy system optimization scheduling method considering multiple heat supply modes of combined heat and power generation, and the method comprises the steps: building an optimization scheduling model which comprises a combined heat and power generation unit full-mode operation model, a heat supply network model and a building model, wherein the combined heat and power generation full-mode operation comprises an extraction condensing operation mode, a heat supply network operation mode and a building operation mode; and adopting a back pressure mode operation and a switching operation mode, and giving out constraint conditions under full-mode operation. Finally, a Fortuny-Amat-McCarl method is adopted to convert the proposed optimal scheduling method into a mixed integer quadratic programming model for solving, so that collaborative optimization of the electric heat energy flow under the full-mode operation of the cogeneration unit is realized, and the flexibility and economical efficiency of the comprehensive energy system are improved.

The invention provides a high-quality task allocation and service deployment method for a mobile group awareness system in an edge computing environment. The method comprises the following steps: S1) communication connection between a user and an edge server; S2)task submission and allocation; S3) establishment of a target function; and S4) service deployment and task allocation. The method is advantageous in that, under the constraint of budget of application program developers, available resources of edge servers and user capacity, a mixed integer quadratic programming (MIQP) mathematical model with task quality and cost balancing as the target is provided, and the task completion quality can be improved as much as possible on the basis of ensuring that the total excitation cost is within a certain limit range and the task can be completed.

The invention provides a low-cost task allocation and service deployment method for a mobile group awareness system in an edge calculation environment. The method comprises the following steps: S1) communication connection between a user and an edge server; s2) submitting and distributing tasks; s3) establishing a target function; and S4) service deployment and task allocation. The method has the advantages that under the constraint of budget of application program developers, available resources of edge servers and user capacity, a mixed integer quadratic programming (MIQP) mathematical model with task quality and cost balancing as a target is provided. The system operation cost can be reduced as much as possible on the basis of ensuring the task completion quality.

The invention provides a power distribution-gas network distributed collaborative programming method and system considering influences of a power transmission network. The method comprises the following steps: obtaining typical operation scenes of power distribution-gas networks; according to the obtained typical operation scenes and a preset power distribution-gas network distributed collaborative programming model, obtaining an optimal distributed collaborative programming scheme of the power distribution-gas networks, wherein the power distribution-gas network distributed collaborative programming model comprises a power transmission network sub-problem mixed integer quadratic programming model, a power distribution network sub-problem mixed integer quadratic programming model and a gas distribution network sub-problem mixed integer quadratic programming model; and sequentially carrying out distributed iterative solution on the three mixed integer quadratic programming models by utilizing an analytical target cascading algorithm. According to the invention, by establishing the mixed integer quadratic programming models of power transmission networks, power distribution networks and gas distribution networks, a distributed solving process based on the analytical target cascading algorithm is constructed, and distributed collaborative programming of multiple power distribution-gas networks is realized.

The invention discloses a damping adjustable semi-active suspension hybrid model predictive control method based on damping force constraints. The method comprises steps of establishing a semi-active suspension hybrid system model; based on the model predictive control theory, performing the finite time domain optimal control of the semi-active suspension hybrid system; and transforming it into a mixed integer quadratic programming problem with real and binary variables, to achieve the hybrid model predictive control of the damping adjustable semi-active suspension. The method has the advantages of solving the contradiction between comfort and handling stability; avoiding the nonlinear constraint condition in the semi-active suspension optimization method; converting the semi-active suspension control problem into the finite time domain constrained optimal control problem; and effectively solving the rolling time domain optimization problem with nonlinear constraints.