63 results about "Immune genetic algorithm" patented technology

The invention relates to a method which can diagnose the soft fault of an analog circuit based on an improved BP neural network. The method comprises the following steps that: the excitation signal and the test node of the analog circuit are selected by adopting the random sampling technique, then a circuit to be tested is applied with the excitation signal and the voltage value is extracted at the test node, and then through principal component analysis and the normalization processing, the characteristic value of the soft fault is extracted and is taken as a training sample; the BP network is optimized by adopting the immune genetic algorithm; the training sample is input into the optimized BP network to realize the training to the network; the practical measured signal of the circuit to be tested is input into the trained optimal BP neural network after being extracted with fault characteristics, and the output of the network is of a fault type. The invention effectively processes the fault diagnosis difficulty of the analog circuit, which is brought out by the tolerance, and improves the efficiency and the performance of the BP network in the analog circuit fault diagnosis.

The invention relates to a real-time path planning method of AUV (Autonomous Underwater Vehicle), in particular to a method for carrying out online, real-time local path planning according to an online map in an AUV real-time collision preventation process. The method comprises the steps of: setting the quantity of small populations according to the quantity of path points of the AUV, initializing; carrying out immune selection on each small population to obtain subgroups; carrying out genetic manipulation on one subgroup, carrying out cell cloning on the other subgroup; then clustering through a vaccination and an antibody to form the next generation of small population, judging whether the next generation of small population meets the conditions or not; if yes, selecting optimal individuals of the small populations; and selecting the optimal individuals from the set consisting of all optimal individuals to be used as a planning path. According to the invention, the diversity of the population is maintained by using an antibody clustering principle, the premature convergence of an algorithm is avoided, and the global optimization is facilitated. The established immune genetic algorithm is used for clustering and analyzing generated filial generations by adopting a self-regulating mechanism, and the diversity of the population is ensured.

The invention relates to a multi-robot task allocation and path planning method. The method comprises: step one, information of multiple robots and tasks is initialized; to be specific, initial state parameters of robots and task information in an environment are obtained; and positions, speeds, maximum power of the robots and task locations are obtained; step two, multi-robot task allocation is carried out; to be specific, comprehensive cost data between all robots and tasks are calculated, factors including the power, working time, and maximum task numbers of the robots are considered, and tasks are allocated to the robots based on a criterion of comprehensive cost minimization; and step three, a robot path planning method is executed; to be specific, path planning of robots is carried out by using an improved immune genetic algorithm. With the method provided by the invention, multiple tasks can be distributed to multiple robots effectively; and the improved immune genetic algorithm is implemented during the robot path planning process to reduce the iteration frequency of the algorithm, thereby improving the searching efficiency of the globally optimal solution of the immune genetic algorithm and obtaining a short optimal solution.

The invention discloses an industrial big data driven total completion time prediction method and relates to the field of engineering application. The method includes: establishing an industrial big data analysis platform; applying an association rule mining algorithm to analyze and mine total completion time influence factors; establishing a neural network model BP; dynamically improving a weight and a threshold of the neural network model BP to acquire a dynamical neural network model DBP; applying an AIGA (adaptive immune genetic algorithm) to optimize the dynamical neural network model DBP so as to acquire a prediction model AIGA-DBP, and computing a total completion time prediction value according to the prediction model AIGA-DBP; when an error of the total completion time prediction value and a total completion time expectation value meets preset conditions, outputting the total completion time prediction value. By the method, the total completion time can be predicated accurately, the work flow of enterprises is optimized, the production efficiency of the enterprises can be improved, and the method is adaptable to various changes of the enterprise due to time lapse.

The invention discloses an opportunity constraint planning-based commercial park comprehensive energy system optimization scheduling method. The method comprises: firstly, constructing a commercial park comprehensive energy system model, and on the basis of modeling of energy conversion of all elements in a park, considering uncertainty of new energy output and load, and establishing a commercialpark energy optimization scheduling model based on opportunity constraint planning. The model aims at minimizing the operation cost. An improved immune genetic algorithm and a stochastic simulation hybrid algorithm are adopted for solving, quantitative indexes are established for accompanying unbalance risks in an opportunity constraint planning model, reference is expected to be provided for comprehensive energy system operation scheduling for balancing economy and reliability, and the method has important guiding significance for actual scheduling of the system.

The invention discloses a practical layering and zoning reactive power optimization method on the basis of power grid real time data in the technical field of the reactive power optimization of a power grid, which comprises the following steps of: reading in data information which comprises a power grid static parameter, a constraint condition and the power grid real-time data; acquiring effective data for power flow calculation from the read data information; determining an optimization mode and screening the effective data according to the optimization mode; and calculating to obtain a reactive power optimization result by adopting a reactive power optimization method on the basis of an improved immune genetic algorithm. In the method, the number of nodes in an area is effectively controlled by layering according to the voltage class or zoning according to a designated area, so that the calculating time is greatly shortened. Moreover, due to the adoption of the improved immune genetic algorithm, the problem of involving in local optimal solution to converge in advance is avoided.

The invention discloses a compressed sensing image reconstructing method based on a prior model and 10 norms, mainly used for solving the defects of poor visual effect and long operation time existing in image reconstruction in the prior art. In the technical scheme of the invention, a compressed sensing image reconstruction frame with 10 norms is optimized by utilizing a prior model; and the positioning of sparsity coefficient and solution of the sparsity coefficient value are achieved through two effective steps: step 1, establishing the prior model, and carrying out low frequency coefficient inverse wavelet transform so as to obtain an image with a fuzzy edge, determining the position of the edge by edge detection, and searching the position of wavelet high frequency subband sparsity coefficient through an immunization genetic algorithm by using the prior model of which the wavelet coefficient has inter-scale aggregation; and step 2, solving a corresponding high frequency subband by using an improved clone selective algorithm, and then carrying out the inverse wavelet transform so as to obtain a reconstructed image. Compared with the prior art, the method has the advantages of good visual effect and low calculation complexity, and can be used in the fields of image processing and computer visual.

The invention discloses a DSP failure diagnostic system based on an immunity genetic algorithm, which consists of an FTU, a RTU, a DSP and an industrial controlling upper computer of a failure signal output display unit. The immunity genetic algorithm comprises the steps of: preparing a parameter code; setting an initial group; designing an adaptability function; setting a matching set; setting a controlling parameter; and solving iteration. The application of DSP failure diagnosis based on the algorithm comprises the steps of: taking the DSP as a core control process system; taking a discrete signal as an input signal of the DSP core control process system when the FTU and the RTU monitor the variable quantity of failure power frequency; and transferring a result which is obtained by the algorithm into a failure message in a practical system through the DSP core control process system to be showed on a monitoring upper computer. The invention has the advantages of: (1) solving the problem of the failure diagnosis of an electrical power system when a protection message is imperfect; and (2) inducing the application of an immune system-matching set to improve the whole searching capability of the algorithm.

The invention relates to a method for predicting coke quality and optimizing a coal blending ratio for tamping coking. The technical scheme of the invention comprises the steps: 1, building a single coal quality index database and a coke quality index database; 2, building a cooperation coal quality index prediction model; 3, building a coke quality index prediction model through employing the technology of support vector machines; 4, building a fitness mathematic model; 5, searching an optimal coal blending ratio through a dynamic vaccine extraction immunization genetic algorithm and the fitness mathematic model; 6, carrying out coal blending according to the optimal coal blending ratio, and carrying out charging and coking after tamping. The method employs the technology of support vector machines for modeling, is high in model prediction speed, is higher in prediction precision, can quickly obtain high coking quality and low-cost coal blending ratio through the dynamic vaccine extraction immunization genetic algorithm, achieves quick optimization of the coal blending ratio, improves the prediction precision of coking quality, improves the coking quality, and reduces the coal blending cost.

A method based on immune genetic algorithm for solving the first kind of assembly line balancing problem is presented. The method comprises steps: a function Psi is defined to enable individuals in apopulation to satisfy assembly precedence relation constrains; the constraint conditions and objective functions of the first kind of assembly line balancing problem are given; finally, an immune genetic algorithm is constructed to solve the first kind of assembly line balancing problem. The method includes the function Psi Definition, Constraints, Objective Function, Immune Genetic Algorithm; theimmune genetic algorithm includes: setting parameters, initializing, updating population, updating memory, terminating condition verification and result output. The invention combines the artificialimmune algorithm and the genetic algorithm to establish a hybrid algorithm with better comprehensive performance, which can be used for solving the assembly line balancing problem of the first type, for example, for solving the assembly line balancing problem of the APXV9R20B antenna.

The invention relates to an identification method of multiple-motion imaging EEG signals. The method comprises the steps of inputting a one-directional characteristic vector which is obtained throughpreprocessing and extraction fusion on a multiple-motion imaging EEG signal into a multi-core learning support vector machine, realizing identification according to an output classification result, wherein preprocessing comprises eliminating noise and ocular artifact, and extraction fusion comprises extracting a time frequency characteristic and a spatial domain characteristic by means of discretewavelet transform and one-to-many common space mode, and connecting the characteristics of the domains in an end-to-end manner for forming the one-dimensional characteristic vector; and obtaining parameters required for identifying the to-be-identified multiple-motion imaging EEG signal through an immune genetic algorithm. The identification method according to the invention effectively overcomesdefects of insufficient information description in a traditional single-domain characteristic extracting algorithm and relatively low identification rate of a single-core classifier. Through using multiple core corresponding fusion characteristics for corresponding with the characteristics of different domains, better robustness and relatively high identification rate of the identifier are realized.

The invention discloses a single-vehicle streamline logistics transportation dispatching method on the basis of an immune genetic algorithm. Multi-yard single-vehicle streamline production logistics transportation dispatching can be implemented by the aid of the single-vehicle streamline logistics transportation dispatching method. The single-vehicle streamline logistics transportation dispatching method includes building multi-yard single-vehicle streamline logistics transportation dispatching models; determining basic factors, including production logistics transportation dispatching time windows, delivery periodicity, goods relevance, yard diversity and the like, of logistics transportation dispatching according to characteristics of streamline production modes; building mathematical models containing objective functions and constraint conditions; designing the immune genetic algorithm for the mathematical models; solving the mathematical models by the aid of the designed immune genetic algorithm. The single-vehicle streamline logistics transportation dispatching method has the advantages that streamline production logistics transportation dispatching can be effectively implemented for a multi-vehicle single delivery center by the aid of the designed algorithm, the solution quality of the algorithm can be improved to a certain extent, and the convergence rate of the algorithm can be increased to a certain extent.

The invention discloses a wastewater treatment energy conservation optimization method based on an improved local search immune genetic algorithm. The method comprises following steps: designing a proportional-integral controller for dissolved oxygen concentration in an aeration tank and nitrate nitrogen in an anoxia pool; determining an energy conservation optimization function and an energy conservation target in consideration of water output and energy consumption; proposing an improved local search immune genetic algorithm; solving dynamically controlled set values of dissolved oxygen and nitrate nitrogen; minimizing and optimizing energy consumption cost on the premise that the water output satisfies the emission standard. According to the method, the improved local search immune genetic algorithm is adopted; an optimized target function with minimum blowing machine energy consumption and pumping energy consumption and with wastewater quality being as a constraint is established; a cycle time of a quarter of a day which is used as the controller reference value is seen as the controlled object, and a dynamically adjusted optimizing strategy is carried out; optimized results of the local search immune genetic algorithm are added to the off-line solving of the controller reference values, and therefore, energy consumption in wastewater treatment is remarkably reduced on the premise that water output quality is guaranteed.

The present invention discloses a back-propagation (BP) neural network immune genetic algorithm based microbial fermentation optimization method which is characterized by comprising the following steps: establishing a microbial fermentation dataset, constructing a BP neural network, using the training dataset to train the BP neural network, performing binary coding on microbial fermentation control parameters, executing a vaccine extraction operator on an initial population, executing a crossover operator, executing a mutation operator, executing a vaccine inoculation operator, taking the BP neural network that is qualified in the training in step four as a fitness function to calculate the fitness value of each individual in the population, executing an immune detection operator, calculating the concentrations of the individuals, executing an immunologic balance operator, and finding optimum individuals from a new population generated by fermentation. The BP neural network immune genetic algorithm based microbial fermentation optimization method can be used for obtaining an optimum control parameter combination according to the existing fermentation data, and the redesign an experiment is not needed.

The invention belongs to the field of photovoltaic power generation experimental devices and particularly relates to an experimental device and method for tracking photovoltaic maximum power based on an immune genetic algorithm. The experimental device comprises a solar cell panel, a storage battery, a controller, a power component, a load and a monitoring platform; a voltage sensor, a current sensor, a temperature sensor and a radiation quantity sensor transmit signals of voltage, current, temperature and radiation quantity of the solar cell panel to the monitoring platform, and the voltage, the current, power, the temperature and the radiation quantity of the solar cell panel are displayed through a computer on the monitoring platform; output ends of the voltage sensor and the current sensor are connected with the controller, and the controller figures out a maximum power point and the voltage through a self-adaption immune genetic algorithm, outputs a PWM signal to the power component, generates disturbance voltage by changing duty ratio of the PWM signal and controls the output of the solar cell panel to reach the maximum power point.

The invention discloses a large-scale road network group traffic induction task decomposing method, and belongs to the field of smart traffic, in particular to a large-scale road network group trafficinduction task decomposing method capable of changing signal timing dial. In a conventional path induction, a single function is difficult to describe a mutual relation among a plurality of variables. A plurality of target functions are established for optimal processing. The problem is solved by adopting an immune genetic algorithm, and the immune genetic algorithm is combined with an optimal heuristic search algorithm which adopts an immune theory and a genetic algorithm, and the advantages of the two kinds of algorithms are kept. The problem that the genetic algorithm is lost in local optimal solution is solved, the immune genetic algorithm has search feature, the optimal self-adaptive feature is solved by using a target function, and over-quick local convergence is avoided. A large-scale road network is decomposed into a plurality of induction cells, an induction strategy based on a tree structure chart is adopted, and an induction task is decomposed layer by layer for building aroad network to serve as a specific example for verifying the effectiveness and superiority of the method.

The invention provides a method for selecting target networks for multi-mode terminals according to the parallel immune genetic algorithm, which is characterized by comprising the following steps: (S1) the multi-mode terminals send the service quality value in each kind of wireless network to a base station; and (S2) the base station receives each service quality value, and calling the parallel immune genetic algorithm to select the target network for each multi-mode terminal. Due to the adoption of the parallel immune genetic algorithm, the problem of single memory unit is avoided, the problem on long delay caused by immaturely-converged algorithm used to select the target networks, the computing time of the algorithm is improved, and the computing time is shortened.

The invention relates to an intelligent video tracking method based on immune genetic particle filtering, which comprises the following steps: initializing and sampling particles, calculating the weighted value of the particles, estimating and outputting the state, calculating the number of effective particles, and if the number of effective particles is smaller than the preset threshold, optimizing the set of immune genetic particles, otherwise, directly sampling particles again. The invention adopts the principle of the immune genetic algorithm, and realizes the optimization of the particle set through fitness calculation, memory unit updating, inhibition of the antibody concentration, promotion, crossover, variation and other types of operation, so that novel particles can be generated on the premise that the effectiveness of particles with high weighted values is maintained, thereby improving the diversity of particle sets and the number of effective particles. The novel particles can better express the true state of the target, thereby enhancing the robustness and effectiveness of the intelligent video tracking and reducing the risk of mistaken tracking.

The invention discloses a multi-phase orthogonal code generating method based on the improved immune genetic algorithm, belongs to the field of radar transmitted waveform generation, and relates to the multi-phase orthogonal code generating method. An orthogonal multi-phase code set is modeled, an initial population is established, a self-adaptation weighting coefficient is calculated, the genetic algorithm selecting, crossing and mutation operation is carried out, the information entropy H and the similarity A of the individual are calculated, group updating is carried out, and a memory unit is updated. The memory function of the immune algorithm is introduced, the subsequent selecting method is adopted for the effect of the increasing of the number of sequences on the multi-phase orthogonal code set performance, the algorithm has good convergence, the diversity of the group is kept, and the performance superior to the performance of original algorithms is obtained.

The invention discloses a control method of an integrative interruption maintenance plan. The control method comprises the steps of tracking and monitoring equipment state, comparing with data stored in a central processing unit (CPU) by integrally online monitoring and live detecting input equipment state data, and determining whether the maintenance is necessary, wherein the CPU obtains a resource scheduling fitness matrix according to the maintenance state and the fitness relation between the interruption maintenance benefit, efficiency in a maintenance resource number determining region and the interruption maintenance task; the CPU determines an optimized objective and constraint conditions according to an interruption rule and target setting; the CPU encodes the task set and randomly generates an antibody; and the CPU adopts calculates by adopting an efficient immune genetic algorithm so as to obtain the optimization result in global convergence. The global co-allocation of the resource and the task in the maintenance plan is realized, the resource scheduling benefit and efficiency of the interruption inspection are improved, and the time, cost and price are reduced.

The invention discloses a crowd sensing task distribution method based on double time limits. The method comprises the following steps that S1, a task publisher sends a sensing task with the time limits to a sensing service platform; S2, sensing participants upload personal information of themselves to a sensing service platform; S3, after the sensing service platform receives the sensing task setand the information of the participants, an immune genetic algorithm is operated to obtain the pairing condition of the participants and the tasks; s4, the sensing service platform sends the task information to the selected participants and provides a task completion sequence; s5, the selected participant executes the sensing task and sends a perception result to the perception service platform;and S6, the perception service platform integrates the received sensing results, returns the perception results to the task publisher, and pays for the participants. According to the method, under theconditions of budget constraint and dual-time limitation, the benefit obtained by the sensing platform is highest.

The invention provides a method for optimizing a structure of a shaft part based on an immune genetic algorithm. The method is implemented by using a computer and comprises the following steps of: converting a combination function correspond into antigens, wherein the combination function is formed by combining the volume of the shaft part with the sensitivity of reliability for a design variable by an image set method; converting the basic size of the shaft part into antibodies; performing immunoselection on the antibodies based on the expected reproductive rate of the antibodies; after performing cloning, crossing and variation operation on an antibody population, making the antibody population generate a progeny antibody population by adopting advantageous measures such as a mating strategy, an elitist strategy, new thought of comparing and substituting similar antibodies, a measure of dividing a memory pool into two parts and the like; and therefore, optimizing the basic size of the shaft part through repeated generation continuation genetic evolution. The method provided by the invention has the advantages of efficiently optimizing the shaft part, improving optimization efficiency and precision and reducing cost. At the same time, the invention provides an effective method for calculating the reliability of the shaft part and the sensitivity of the design variable.

The invention provides a boiler combustion control system and method, and belongs to the technical field of boiler combustion control. The system comprises a receiving module, a processing module anda control module, and the receiving module is used for receiving boiler combustion parameters every moment in the period from the time k-n to the time k; the processing module is used for acquiring the control quantity of the opening degree of a secondary air door of a boiler at the time k+1 with an immune genetic algorithm through optimization according to the boiler combustion parameters including the opening degree of the secondary air door in the period from the time k-n to the time k; and the control module is used for adjusting the opening degree of the secondary air door of the boiler at the time k+1 according to the control quantity of the opening degree at the time k+1 every moment, wherein n is larger than 0. According to the boiler combustion control system, the optimal openingdegree of the secondary air door is obtained with the immune genetic algorithm through optimization of the opening degree of the secondary air door in the past period of time, and the opening degree of the secondary air door is adjusted accordingly; the opening degree of the secondary air door is adjusted according to the technical scheme provided by the invention, and the emission of nitrogen oxides is reduced greatly while the boiler combustion efficiency is improved.

The present invention relates to a modularization multi-level converter modulation method based on an immune genetic algorithm, research on the optimizing control problems of a modularization multi-level converter is carried out, and an optimizing target is to output an optimal current waveform of the modularization multi-level converter. The method is characterized by: with combination of structural characteristics and output properties of the modularization multi-level converter, using the number of sub-modules invested into a bridge arm in each phase to demonstrate working condition; carrying out optimization on the working condition of the modularization multi-level converter; and through an encoding operation, vaccine injecting, affinity calculation, concentration calculation, immunity selection, an interlace operation, a mutation operation, and an inversion operation, carrying out multiple iteration convergence to obtain an optimized output voltage step wave of the modularization multi-level converter. According to the method disclosed by the present invention, through the immune genetic algorithm, a modulation strategy to the modularization multi-level converter is optimized, and the method has advantages of simple calculation, fast convergence speed, and easy implementation.

The invention provides a modular process recombination method based on an improved genetic algorithm. According to the invention, a product demand value module process recombination set containing a plurality of process routes can be obtained through calculation. The method has creative breakthrough. The method comprises the following steps: randomly generating an initial population with a certainscale; and then combining an immune genetic algorithm and an elite retention strategy, and replacing the individual with the worst fitness in the randomly generated initial population with an elite antibody, so that the diversity of the initial population can be ensured, the number of later populations can be improved, and excellent individuals are replaced and guide the population to evolve in the genetic process to quickly approach a target value. By adopting the method to carry out modular process generation, a plurality of process recombination sets with high satisfaction can be obtained,the process decision time is shortened, and the productivity is improved.

A multi-SMES coordinated control system and method based on an immune genetic algorithm is characterized by comprising a voltage and current detection circuit, an A / D sampling and converting module, a DSP control unit, two PWM drive units and two SMES modules. The work method includes the steps of signal collecting, processing, converting and outputting. The system and method have the advantages that a hardware device is convenient to control, output of the two SMESs is controlled by the adoption of the immune genetic algorithm, the speed is higher, and real-time control capacity of a controller is improved.

The invention discloses a single-piece workshop scheduling method based on an immune genetic algorithm. The method comprises the following operation steps of: 1, determining operation parameters, 2, generating an initial population; 3, performing individual fitness calculation; 4, updating a memory cell bank; 5, evaluating an individual concentration; 6, performing individual promotion and inhibition; 7, performing crossover operation; 8, performing mutation operation;9, extracting a vaccine; 10, performing vaccination; 11, performing immunoselection; 12, adopting an elitism strategy; and 13,terminating discriminating. When the method provided by the invention is adopted to solve single-piece workshop scheduling, a satisfactory scheduling scheme can be more effectively solved, and the production efficiency is improved.

The invention discloses a receiving-end power-grid dynamic reactive optimization method containing a voltage infeasible node and an apparatus thereof. The method comprises the following steps of S1, acquiring a reactive compensation node in a receiving-end power grid; S, taking a condition that whether a voltage constraint infeasible node is generated in one time period as a basis, carrying out circularity detection on each time period and acquiring a plurality of infeasible period clusters and feasible period clusters of a whole day; S3, in the infeasible period clusters, taking a reactive compensation node as a basis, dividing a receiving-end power grid into a voltage control area and a non-voltage control area and constructing dynamic reactive optimization submodels of the two areas respectively; S4, using a parallel coevolution algorithm to solve the dynamic reactive optimization submodel of the infeasible period clusters, based on a result of the infeasible period clusters, using a coordination strategy taking the infeasible period clusters as a center and then using an immune genetic algorithm to solve a conventional dynamic reactive optimization submodel for the feasible period clusters; and combining dynamic reactive optimization results of the feasible period clusters and the infeasible period clusters.

The invention discloses a space fragment removing system and method and a space fragment removing task planning method. The space fragment removing method comprises the steps that a task satellite carries a plurality of pushing off-track devices to the positions of target fragments; the task satellite releases one pushing off-track device, and the pushing off-track device pushes the target fragments to a grave track; and the task satellite maneuvers to the next target fragment position, and the above operation is repeatedly conducted. The space fragment removing task planning method comprisesthe steps that an immune genetic algorithm is adopted for top-layer optimization, and an optimal task sequence is obtained; and a particle swarm optimization algorithm is adopted for conducting bottom-layer optimization, and an optimal transferring track is obtained. According to the space fragment removing system and method and the space fragment removing task planning method, the task satelliteonly needs to maneuver between the multiple to-be-removed fragments, the optimal task sequence through which the fuel consumption of the whole task is minimum and the number of carried optimal off-track pushing devices can be obtained, the fuel consumption in the fragment removing process is low, and the efficiency is high.

The invention provides a power distribution network reactive power optimization method and device based on an immune genetic algorithm. The method mainly comprises the steps of: obtaining initial antibodies according to preset power distribution network system parameters and immune genetic algorithm parameters, and carrying out the iterative calculation of the initial antibodies; updating the affinity of each initial antibody through a preset power flow algorithm in iterative calculation, and performing evolution operation on the initial antibodies according to the updated affinity until the number of times of iterative calculation reaches a preset value to obtain iterative antibodies; and calculating a reactive power optimization result of the power distribution network according to the iterative antibodies, and performing reactive power optimization on the power distribution network according to the reactive power optimization result. The antibodies are iterated and the power distribution network is optimized through the improved immune genetic algorithm, so that the reactive capacity is minimum while the voltage loss of a compensation line is ensured. Compared with the prior art, the method and device make the reactive power distribution of the power distribution network system more reasonable, and have better safety and stability.