Artificial intelligence techniques in power systems

2011 ◽  
pp. 1-18 ◽  
Author(s):  
M. Laughton
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Artificial Intelligence Techniques

scholarly journals Application of artificial intelligence techniques for LFC and AVR systems using PID controller

2019 ◽  
Vol 10 (3) ◽  
pp. 1694 ◽  
Author(s):  
Ghassan Abdullah Salman ◽  
Assama Sahib Jafar ◽  
Ammar Issa Ismael
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Pid Controller ◽  
Fitness Function ◽  
Load Frequency Control ◽  
Frequency Deviation ◽  
Mathematical Methods ◽  
Artificial Intelligence Techniques ◽  
Integral Time ◽  
Optimal Values

Development of electrical power systems led to search for  a new mathematical methods to find the values of PID (Proportional-Integral-Derivative) controller. The goal of the paper is to improve the performance of the overall system, through improved the frequency deviation and the voltage deviation characteristics using PID controller, so in this paper are proposed three methods of artificial intelligence techniques for designing the optimal values of PID controller of Load-Frequency-Control (LFC) and Automatic-Voltage-Regulator (AVR), the first is the Firefly Algorithm (FA), the second is the Genetic Algorithm (GA) and the third is the Particle Swarm Optimization (PSO), in addition to these three methods use the conventional (Ziegler–Nichols, Z-N). The FA, GA and PSO are used to obtain the optimal parameters of PID controller based on minimized different various indices as a fitness function, these fitness functions namely Integral-Time-Absolute-Error (ITAE) and Integral-Time-Square-Error (ITSE). Comparison between the results obtained show that FA has better performance to control of frequency deviation and terminal voltage than GA and PSO, so the results observed the FA is more effectual and reliable to determine the optimal values of PID controller.

Analysis of Electric Load Forecasting using Artificial Intelligence

2018 ◽  
Vol 4 (5) ◽  
pp. 10
Author(s):  
Srashti Shrivastava ◽  
Dr. Krishna Teerth Chaturvedi
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Power Systems ◽  
Work Performance ◽  
Research Work ◽  
Load Forecasting ◽  
Energy Markets ◽  
Estimation Errors ◽  
Artificial Intelligence Techniques ◽  
Learning Machine

Electricity demand forecasts are extremely important for energy suppliers and other actors in production, transmission, distribution and energy markets. Accurate models for predicting the load of electricity are critical to the operation and planning of a service company. load forecasts are extremely important for energy suppliers and other actors in production, transmission, distribution and energy markets. short-term load forecasts play an important role in the operation of power systems to ensure an immediate balance between energy production and demand. The accuracy of the prediction generated by the artificial intelligence has several factors, including, but not limited to, what is used to form the network algorithm, how much and which type of data are used in the network’s training set. In this research the best combination has been investigated of these factors to decrease performance parameters to give the best forecast possible. Artificial intelligence techniques have gained importance in reducing estimation errors. Artificial neural network, Extreme Learning Machine and Decision tree such as LSBOOST and RF are among these artificial intelligence techniques. That are used in this research work for performance analysis. In this work, performance evaluation metrics such as MSE, RMSE, MAE and MAPE values are analysed and it is concluded that Random forest decision tree forecasting algorithm gives better performance of forecasting as compared to other artificial intelligence algorithms for 24 hours load forecasting as well as for 7 day load forecasting.

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