Gas turbines are increasingly spread throughout the world to provide mechanical and electrical power in consumer and industrial sections. To ensure an accurate control process temperature of gas turbine with no extortionary operator involvement, a proper controller is required. Load frequency control of gas turbine is also regulates the power flow between different areas while holding the frequency constant. The main idea in this study is to assemble these 2 controllers in a unit work; the area of robust control has grown to be one of the wealthy in terms of algorithms, design techniques, analytical tools and modifications. Several books and papers already exist on the topics of parameter estimation and adaptive control. In The proposed approach, a robust and evolutionary based Proportional, Integral, Derivative (PID) is utilized to control frequency-response and a robust evolutionary based Proportional, Integral (PI) is utilized to control temperature. The evolutionary algorithm is used to make an optimal Proportional-Integral-Derivative (PID) controller Tuning parameters. The new robust PID controller is compared with a normal classic controller (Ziegler-Nichols) designed by the method.
This paper proposes an Imperialist Competitive Algorithm (ICA) for optimal multiple distributed generations (DGs) placement and sizing in a distribution system. The objective is to minimize the total real power losses and improve the voltage profile within real and reactive power generation and voltage limits. Three types of DG are considered and the ICA is used to find the better sizes and locations of DGs for maximum real power losses reduction and voltage improvement for given number of DG units in each type. Both integer and continuous variables are considered in ICA, integer variable for locations and continues variable for sizes. The total real power losses and voltage profile evaluation are based on a power flow method for radial distribution system with the representation of DGs. The proposed method has been demonstrated on 33 bus radial distribution system. The efficiency of the ICA in reducing the total power losses and improving voltage is validated by comparing the obtained results with Particle Swarm Optimization (PSO) algorithm.
Nowadays, automatic generation control (AGC) is a very impressive problem in restructured power system operation for providing electric power with high quality and reliability. In this paper, the three-area multi-units system has been selected for AGC in a restructured power system. Federal energy regulatory commission supports an open market system for price-based operation. The theory of DISCO participation matrix is used to simulate the bilateral contracts in the three-area multi-units systems. In a restructured system because of open market system the power flow between areas is varying continuously however the system must be able to maintain stability in all conditions. In this paper, two cases for design robust controller have been considered. A new method using the imperialist competitive algorithm for designing this controller has been used. Finally, the obtained results are compared with other methods.
Power companies world-wide have been restructuring their electric power systems from a vertically integrated entity to a deregulated, open-market environment. Previously, electric utilities usually sought to maximize the social welfare of the system with distributional equity as its main operational criterion. The operating paradigm was based on achieving the least-cost system solution while meeting reliability and security margins. This often resulted in investments in generating capacity operating at very low capacity factors. Decommissioning of this type of generating capacity was a natural outcome when the vertically integrated utilities moved over to deregulated market operations. This study proposes an optimizing base and load demand relative binding strategy for generating power apprises of different units in the investigated system. Afterwards, congestion effect in this biding strategy is investigated. The described systems analysis is implemented on 5 and 9 bus systems and optimizing technique in this issue is the Invasive Weed Optimization algorithm; the results are then compared by GA. Finally, examined systems is simulated by using the Power World software; experimental results show that the proposed technique (Invasive Weed Optimization) is a high performance by compared GA for the congestion management purposes.
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