This paper presents how to design proportional integral controller and Fuzzy-PI based controller for efficiently load frequency control. Loads on the electrical system always vary in relation to that time, which results in diversity of frequency, causing frequency control problems to be loaded. The frequency difference is highly undesirable and the maximum allowable difference in frequency is ± 0.5 Hz. This paper load frequency control is done by PI controller, which is a conventional controller. This type of controller is slow and the controller does not allow the designer to keep in mind the potential change in operating conditions and non the generator unit. To overcome these flaws, new intelligent controllers like Fuzzy-PI Controller are presented to extinguish tie-line power due to deviation in frequency and various load disturbances. The effectiveness of the proposed controller has been confirmed using the MATLAB / SIMULINK software. The results show that the PI-fuzzy controller provides fast response, little undershoots and negligible overshoot with small state transfer time to reach the final stable position.

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This paper presents a comparative study of P and PI controllers for a current source inverter (CSI) fed induction motor drive system. A dq model has been used which incorporates the induction motor and the inverter power supply with current feedback. The model is used first to generate the steady state curves to determine the operating point through computer simulations using the software package MATLAB. Then a transient analysis has been carried out for different values of the speed and current controller parameters. The controller value is adjusted by the Ziegler-Nichols method. It has been observed that the transient time to reach the steady state value is larger with the PI controller than with the P controller.

The electric power system network is rapidly becoming more and more complex to meet energy requirements. With the development of integrated power systems, it becomes all the more necessary to operate the plant units most economically. More recently, soft computing techniques have received more attention and have been used in a number of successful and practical applications. In the chapter, artificial intelligence-based modern optimization techniques, the genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), are used to solve the economic load dispatch related problems. In the chapter, the minimum cost is computed by adopting the genetic algorithm, PSO, and DE using the data from 15 generating units. Data has been taken from the published works containing loss coefficients are also given with the maximum-minimum power limits and cost function. All the techniques are implemented in MATLAB environment. Comparing the results obtained from GA, DE, and PSO-based method, better convergence was found in the PSO-based approach.

The term fuzzy logic has been used in two different senses. It is thus important to clarify the distinctions between these two different usages of the term. In a narrow sense, fuzzy logic refers to a logical system that generalizes classical two-valued logic for reasoning under uncertainty. In a broad sense, fuzzy logic refers to all of the theories and technologies that employ fuzzy sets, which are classes with unsharp boundaries. Fuzzy logic is all about the relative importance of correctness: How supreme is it to be exactly right when a rough answer will do?