Effect of SMES Unit in AGC of an Interconnected Multi-Area Thermal Power System With ACO-Tuned PID Controller

Effect of SMES Unit in AGC of an Interconnected Multi-Area Thermal Power System With ACO-Tuned PID Controller

K. Jagatheesan (Paavai Engineering College, India), B. Anand (Hindusthan College of Engineering and Technology, India), Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India) and Amira S. Ashour (Tanta University, Egypt)
Copyright: © 2018 |Pages: 21
DOI: 10.4018/978-1-5225-4151-6.ch007
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Load changes in any one of interconnected power system that influence the system response from their nominal values. The Proportional–Integral- Derivative (PID) controller is employed to mitigate this issue as a secondary controller in addition to the Superconducting Magnetic Energy Storage (SMES) unit. In Automatic Generation Control (AGC), the current work proposed an Ant Colony Optimization (ACO) technique to tune PID controller gain values of multi-area interconnected thermal power system. The gain value of PID controller is tuned by using the ACO techniques. The system response is compared with and without considering SMES unit in the system. The comparative results clearly established that the system response with SMES unit improve the performance of system during sudden load disturbance.
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Nowadays, the electrical energy demand increases rapidly all over the world due to the enormous development in technology. Large scale power systems are created to achieve energy demand balance. The large scale power systems are incorporated with a number of control areas that are interconnected through the tie lines. In any one of power system, the increased load demand will lead to share the power between the connected areas to maintain the system in stable condition. The size and complexity are increased with large control areas’ interconnections. The system complexity can be reduced with the help of recently developed modern control theory.

Many researchers reported the AGC of interconnected power systems performance that were related to AGC or LFC of two and three equal or unequal areas of thermal-thermal, and thermal-hydro systems. Several studies have been including various optimization techniques (Samanta et al., 2013a; 2013b; Jagatheesan, & Anand, 2014), while others have been directed with controller types with different optimization techniques that have been used in the LFC, such as classical, optimal, fuzzy logic (Ali, & Abd-Elazim, (2011), neural network (Anand, & Jeyakumar, 2009), Variable Structure Control (VSC) (Das et al., 1991), Optimal Control (Moon et al., 2000), and Decentralized Controller (Jagatheesan et al., 2014). In LFC or AGC the optimal PID controller gain values are optimized by using recently developed evolutionary computation techniques, such as ACO (Hsiao et al., 2004), Bacteria Foraging Optimization (BFO) (Ali, & Abd-Elazim, (2011), and Particle Swarm Optimization (PSO) (Ebrahim et al., 2009), Artificial Bee Colony (ABC) (Debbarma et al., 2013), Cuckoo Search (CS) (Dash et al., 2014), and Teaching Learning Based Optimization (TLBO) (Chidambaram, & Paramasivam, 2012).

The AGC in 3-area equal Thermal–Thermal–Hydro system has been investigated with different classical controllers and the performance has been compared with fuzzy Integral double derivative (IDD) controller (Taher et al., 2014). The optimal controllers’ gain values are optimized using BFO associated to conventional PID controller. Imperialist Competitive Algorithm (ICA) is implemented with LFC of 3-area power system with Fractional Order PID (FOPID) controller (Debbarma et al., 2013). The simulation result demonstrated the superiority of system performance with ICA based controller compared to the existing controller. The FOPID controller has been implemented under released environment using BF technique for optimization (Roy et al., 2010). From the above discussion, it is very clear that the modern evolutionary computation techniques have been developed and implemented the three areas system in the load frequency control successfully. An energy storage device with the ability to decrease the system frequency oscillations andthe tie-line power flow within a quick period of time has been presented in (Tam, & Kumar, 1990). Many energy storage units, namely the capacitive energy storage (CES), battery energy storage, SMES and flywheels, have been modeled.

In the current chapter, super conducting magnetic energy storage unit is implemented, which stores the energy in the magnetic field model (Tam, & Kumar, 1990). The stored energy is suddenly released through power conversion system with the rise in load demand. Due to this demand, the turbine and other LFC arrangements are adjusted for the power balance and a new equilibrium. SMES coil absorbs the energy through the system steady state situation. The absorbed energy is released during sudden load changes. The control of SMES unit is based on changing the converter firing angle. Since the operative way of using SMES unit is based on the control strategy method, an efficient soft computing ACO technique is used for tuning the PID controller gain values in AGC of three areas interconnected reheat thermal power systems. The chief aims of the presented work are as follows:

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