On-line security optimisation of large power systems using artificial intelligence techniques

1997 ◽  
Author(s):  
B.A. Nicholson
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Large Power ◽  
Artificial Intelligence Techniques ◽  
On Line

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.

On-line Estimation of Power System Low Frequency Oscillatory Modes in Large Power Systems

2011 ◽  
Vol 1 (3) ◽  
pp. 352-358 ◽  
Author(s):  
Chairerg Jakpattanajit ◽  
Naebboon Hoonchareon ◽  
Akihiko Yokoyama
Keyword(s):  
Power Systems ◽  
Power System ◽  
Low Frequency ◽  
Large Power ◽  
On Line

scholarly journals Fast and Accurate Load Flow Solution for On-line Applications Using ANN

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Wael Abdullah Alsulami
Keyword(s):  
Steady State ◽  
Power Systems ◽  
Flow Analysis ◽  
Load Flow ◽  
Line Load ◽  
Large Power ◽  
Flow Solution ◽  
Load Flow Analysis ◽  
On Line ◽  
Bus Voltage

This paper aims to develop a fast load flow computation technique without sacrificing accuracy for various on-line applications of large power systems. Both planning and operation of any power system requires the conduct of many load flow analyses corresponding to various operating modes with different system loading conditions and network configurations. Load flow analysis is performed for the determination of steady state operating status of power systems in terms of bus voltage magnitudes and angles, real and reactive powers and the transmission line losses. The load flow analysis involves the solution of non-linear algebraic equations and hence the conventional load flow algorithms are iterative in nature. The state-of-the-art approach for load flow analysis is based on Newton-Raphson algorithm (NRLF) or its derivatives such as fast decoupled load flow. As these methods are capable of providing the steady state solution within the specified accuracy, these techniques are effectively utilized as a planning tool by various utilities throughout the world. However, these are seen to be ineffective for on-line computations of practical large power systems because of the significant computational over-head due to the inherent iterative nature of such algorithms. Even though the non-iterative DC load flow approach, derived out of NRLF is computationally faster than the conventional techniques, solution accuracy is significantly less than that of its iterative counterparts. Hence, this paper proposes to develop a fast and accurate approach for the on-line load flow analysis. It is proposed to apply artificial neural network (ANN) technique as these are seen to be non-algorithmic in nature. The multi-layer feed-forward ANN for the load flow solution used in this study has one hidden layer with 100 neurons in addition to the input and output layers.  The real and reactive power demands are given as the inputs to the ANN. The output consists of the bus voltage magnitudes and angles at the load buses. The proposed ANN is trained using the conventional NRLF load flow solution of a practical power grid at various load levels. The investigations revel that the ANN as a potential tool for the on-line load flow solution of practical power systems.  

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