Multi-target reactive power optimization method based on adaptive chaos particle swarm algorithm

A chaotic particle swarm and optimization method technology, applied in reactive power compensation, reactive power adjustment/elimination/compensation, calculation, etc., can solve the problem that multi-peak functions are not searched, limit global search ability, and cannot be adjusted adaptively Weight coefficient and other issues, to achieve the effect of improving voltage quality, avoiding premature convergence, and improving global search ability

Inactive Publication Date: 2014-08-06
STATE GRID CORP OF CHINA +1
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Problems solved by technology

[0004] Particle Swarm Optimization algorithm PSO (Particle Swarm Optimization) is an iterative multi-point random search intelligent optimization algorithm, which has the characteristics of simple operation and less required setting parameters, and has been applied by electric power workers in reactive power optimization. , the current par...

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  • Multi-target reactive power optimization method based on adaptive chaos particle swarm algorithm
  • Multi-target reactive power optimization method based on adaptive chaos particle swarm algorithm
  • Multi-target reactive power optimization method based on adaptive chaos particle swarm algorithm

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specific Embodiment approach 1

[0017] Specific embodiment one: a kind of multi-objective reactive power optimization method based on self-adaptive chaotic particle swarm optimization algorithm of this embodiment includes:

[0018] 1. Input the original data of the particle swarm to the adaptive chaotic particle swarm algorithm program, randomly generate an n-dimensional chaotic vector through the chaotic algorithm, and then calculate N chaotic variables through the Logistic complete chaotic iterative formula;

[0019] 2. Substituting each component of the chaotic variable into the total objective function of multi-objective reactive power optimization to calculate the fitness value corresponding to each chaotic vector, and selecting the first m as the initial position of the particle swarm according to the size of the fitness value;

[0020] 3. Particles are coded by mixed coding of integer and real numbers. According to the control variable value of the particle code, each particle in the initial position o...

specific Embodiment approach 2

[0028] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is that the step one is specifically:

[0029] Input the original data into the adaptive chaotic particle swarm algorithm program, initialize the generator terminal voltage, reactive power compensation capacity and transformer taps in the particle swarm reactive power optimization through the chaotic algorithm, and randomly generate an n-dimensional and each component value is between 0 and The chaos vector Z between 1 1 =(z 11 ,z 12 ,…,z 1n ), with Z 1 is the initial value by the Logistic complete chaotic iterative formula z t+1 =4z t (1-z t )t=0, 1, 2, ..., N chaotic variables Z are calculated 1 ,Z 2 ,…,Z N , using the chaotic variable Z i (i=1, 2, ... N) for iterative search, and then through the formula x ij =a j +(b j -a j )z ij , (i=1, 2..., N; j=1, 2,..., n) the chaotic variable Z i Each component of (i=1, 2, ... N) is transformed...

specific Embodiment approach 3

[0031] Specific implementation mode three: the difference between this implementation mode and specific implementation modes one or two is that the step two is specifically:

[0032] Determine the dimension n of particle swarm particles according to the number of reactive power optimization control variables. In the three types of control variables, namely the generator terminal voltage V G , Transformer tap T t and reactive power compensation capacity Q C Within the upper and lower bound constraints of the chaotic variable Z i Each component of (i=1, 2,...N) is substituted into the total objective function minF=λ of multi-objective reactive power optimization 1 P' loss +λ 2 dV'+λ 3 V' SM Where: λ 1 ,λ 2 ,λ 3 For the weight coefficient of each objective, the three objectives are normalized, and the specific processing form is as follows:

[0033] P loss ′ ...

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Abstract

The invention relates to a multi-target reactive power optimization method, in particular to a multi-target reactive power optimization method based on an adaptive chaos particle swarm algorithm. The method aims at solving the problem that multi-target reactive power optimization control variables are probably trapped in a locally optimal solution, and the speed for acquiring an optimal solution is low. The method includes the steps that firstly, original data of a particle swarm are input to an adaptive chaos particle swarm optimization algorithm program; secondly, first m particles are selected from the particle swarm as initial positions of the particle swarm according to fitness values in a preferred mode; thirdly, inertia weights w of the particles are acquired through calculation of inertia weight coefficients, and first M preferred particles are selected from the particle swarm for chaos optimization calculation; fourthly, the speed and the positions of the particles are updated according to the particle swarm reactive power optimization algorithm, and then iteration allowances and values of the control variables can be acquired; fifthly, whether iteration stop conditions are met or not is judged, and then the multi-target reactive power optimization method based on the adaptive chaos particle swarm optimization algorithm is finished. The multi-target reactive power optimization method is applied to the field of electric systems.

Description

technical field [0001] The invention relates to a multi-objective reactive power optimization method and relates to the field of power system reactive power optimization. Background technique [0002] Power system reactive power optimization is to study when the system structure parameters and load conditions have been given, through the optimization calculation of some control variables in the system, to find the system's power consumption under the premise of satisfying all specific constraints. An operation control scheme when one or more performance indicators are optimal. [0003] At present, there are many methods for solving reactive power optimization. The traditional mathematical programming methods mainly include nonlinear programming and linear programming. The difficulty of the conventional method is mainly the rounding problem of discrete variables, which is easy to fall into local optimum and produce the "curse of dimensionality" problem. In recent years, in ...

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Application Information

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IPC IPC(8): H02J3/18G06N3/00
CPCY02E40/30
Inventor 刘金龙杨琳
Owner STATE GRID CORP OF CHINA
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