Implementation of Genetic-Algorithm-Based Forecasting Model to Power System Problems

2018 ◽  
pp. 140-155
Author(s):  
Sajad Madadi ◽  
Morteza Nazari-Heris ◽  
Behnam Mohammadi-Ivatloo ◽  
Sajjad Tohidi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Power System ◽  
Load Forecasting ◽  
Wind Farms ◽  
Optimization Method ◽  
Forecasting Model ◽  
Renewable Generation ◽  
Forecasting Methods ◽  
Pool Market

Power system includes many types of markets. Such markets are generally cleared at certain times, whereas market participators have to determine their operational plans before meeting the actual conditions. Therefore, forecasting methods can assist market players. Forecasting methods are applied to forecast electricity demand. The unknown conditions in the power system are increased by integration of renewable generation units. Forecasting methods, which are used for the load forecasting, are updated because the output power of renewable generation units such as wind farms and photovoltaic (PV) panels have more deviation than power demand. The pool market can be introduced as other parameter that is forecasted by market players. In this chapter, the authors investigate a mathematical model for forecasting of wind. Then, the forecasting model is proposed. Genetic algorithm is applied as an optimization method to handle delay associated with wind forecasting.

scholarly journals Short-Term Load Forecasting of Power System Based on Improved BP Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power System ◽  
Bp Neural Network ◽  
Normal Load ◽  
Load Forecasting ◽  
System Optimization ◽  
Forecasting Model ◽  
Short Term ◽  
Short Term Load Forecasting

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

Genetic Algorithm Optimization of a Double Four-Bar Manipulator

2013 ◽  
Vol 834-836 ◽  
pp. 1323-1326
Author(s):  
Qi Jing Tang ◽  
Tie Shi Zhao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Structural Strength ◽  
Optimization Method ◽  
Installation Space ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Transmission Angle ◽  
The Mathematical Model

In order to optimize the dimension of a manipulator, the optimization requirements are analyzed. Then the mathematical model and optimization objectives are established. Next, the lengths of the manipulator are optimized by Matlab genetic algorithm optimization toolbox. The structural strength and bearing installation space are considered at the same time. The trajectory and transmission angle are compared. Finally, the lengths which meet the use requirements are obtained. This optimization method provides a reference for similar mechanism.

Technique for Magnetic Bearing Based on Mixed-Kernel Support Vector Machine Forecasting Model

2014 ◽  
Vol 529 ◽  
pp. 349-353 ◽  
Author(s):  
Jie Jin ◽  
Huang Qiu Zhu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Magnetic Bearing ◽  
The Self ◽  
Support Vector ◽  
Forecasting Model ◽  
Kernel Support Vector Machine ◽  
Vector Machines ◽  
Nonlinear Forecasting ◽  
The Cost

The self-sensing magnetic bearing can reduce the cost and the axial size of the magnetic bearing and increase its reliability. A mixed-kernel least squares support vector machines (LS-SVM) forecasting model is proposed for self-sensing technique of a hybrid magnetic bearing. The structure and mathematical model of the radial-axial hybrid magnetic bearing are introduced. Based on the principle of the mixed-kernel LS-SVM, the nonlinear forecasting model between the current and the displacement which realizes the displacement self-sensing control is built through genetic algorithm. Simulation has done to verify the validity and feasibility of proposed method.

scholarly journals An Optimized Method of Wind Speed Prediction with Support Vector Machine and Genetic Algorithm

2021 ◽  
Vol 15 ◽  
pp. 212-217
Author(s):  
Guan-fa Li ◽  
Wen-sheng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Power System ◽  
Prediction Models ◽  
Optimization Method ◽  
Support Vector ◽  
Wind Speed Prediction ◽  
Speed Prediction

Due to the randomness of wind speed and direction, the output power of wind turbine also has randomness. After large-scale wind power integration, it will bring a lot of adverse effects on the power quality of the power system, and also bring difficulties to the formulation of power system dispatching plan. In order to improve the prediction accuracy, an optimized method of wind speed prediction with support vector machine and genetic algorithm is put forward. Compared with other optimization methods, the simulation results show that the optimized genetic algorithm not only has good convergence speed, but also can find more suitable parameters for data samples. When the data is updated according to time series, the optimization range of vaccine and parameters is adaptively adjusted and updated. Therefore, as a new optimization method, the optimization method has certain theoretical significance and practical application value, and can be applied to other time series prediction models.

scholarly journals On the placing of synchronized vector measurements in network 110 - 330 kV for identification of the Kaliningrad region power system regime

2018 ◽  
Vol 58 ◽  
pp. 01005
Author(s):  
Magomed Gadzhiev ◽  
Eugenia Gulevich ◽  
Vyacheslav Korobka ◽  
Vladimir Ryabchenko ◽  
Yuriy Sharov
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Power System ◽  
Electric Power ◽  
Electric Power System ◽  
Phasor Measurement Unit ◽  
Measurement Unit ◽  
Method Of Solution ◽  
Kaliningrad Region ◽  
The Mathematical Model

A solution is presented for the problem of placing phasor measurement unit to identify the mathematical model of the electric power system in the state space. The criterion for complete observability of the Kalman dynamic system is taken as a basis. As a method of solution, we use the canonical genetic algorithm. The complete observability of the power system is ensured by the application of the observability rule in the form of a recursive test of observability. The proposed approach is demonstrated by the arrangement of synchronized vector meters in the Kaliningrad region power system.

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