Susanta Dutta

Load Frequency Control of Interconnected Power System Using Teaching Learning Based Optimization

This paper presents the design and performance analysis of teaching learning based optimization (TLBO) algorithm based PID controller for load frequency control (LFC) of an interconnected power system. A two area reheat thermal system equipped with PID controllers which is widely used in literature is considered for the design and analysis purpose. The design objective is to improve the transient performance of the interconnected system. The power system dynamic performance is analyzed based on time response plots achieved with the implementation of designed optimal and sub-optimal LFC regulators in the wake of 1% load disturbance in one of the areas. The results of the TLBO optimized PID controllers on a two area reheat thermal system are compared with those of artificial bee colony (ABC) and differential evolution (DE) optimized PID controllers. The TLBO optimized controllers are found to be superior in terms of peak transient deviation, settling times, and dynamic oscillations.

Optimal Location of TCSC Using Opposition Teaching Learning Based Optimization

This paper focuses on the optimal power flow solution and the enhancement of the performance of a power system network. The paper presents a secured optimal power flow solution by integrating Thyristor controlled series compensator (TCSC) with the optimization model developed under overload condition. The Teaching Learning Based Optimization (TLBO) has been implemented here. Recently, the opposition-based learning (OBL) technique has been applied in various conventional population based techniques to improve the convergence performance and get better simulation results. In this paper, opposition-based learning (OBL) has been integrated with teaching learning based optimization (TLBO) to form the opposition teaching learning based optimization (OTLBO). Flexible AC Transmission System (FACTS) devices such as Thyristor controlled series compensator (TCSC) can be very effective for power system security. Numerical results on test systems IEEE 30-Bus with valve point effect is presented and compared with results of other competitive global approaches. The results show that the proposed approach can converge to the optimum solution and obtains the solution with high accuracy.

Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices

This paper illustrates, for the first time, the use of artificial bee colony optimization ABC technique to study optimal reactive power dispatch ORPD in power system with the use of flexible AC transmission systems FACTS. FACTS controller cannot only increase the power transmission capacity without installing new transmission lines, but they can also enhance voltage profile and reduce transmission loss in power system. Two types of FACTS devices namely, thyristor-controlled series capacitor TCSC and thyristor-controlled phase shifter TCPS are considered in this paper. A standard IEEE 30-bus test system with multiple TCSC and TCPS devices is used for two different objective functions to validate the performance of the proposed method. The simulation results demonstrates the ability of the ABC to produce better optimal solutions compared to particle swarm optimization with inertia weight approach PSOIWA and real coded genetic algorithm RGA.

Automatic generation control by SMES-SMES controllers of two-area hydro-hydro system

In this paper, oppositional biogeography based optimization (OBBO) algorithm for load frequency control with interconnected two-area hydro-hydro power system have been investigated. As a consequence of continually load variation, the frequency of the power system changes over time. To stabilize the system frequency oscillations, the active power can be controlled via superconducting magnetic energy storage device (SMES). The significant improvement of optimal transient performance is observed with the addition of SMES unit fitted in both areas. Analysis reveals that SMES unit fitted in either of the areas is as effective as SMES units fitted in both the areas and improves the dynamic performances to a considerable extent following a load disturbance in either of the areas. Gains of the integral controller in LFC loop and parameters of SMES are optimized with the help of OBBO technique. The performance of the proposed OBBO based controller is better as compared with BBO, CRPSO and RGO controller in the presence of SMES. The simulation results indicate the superiority of the proposed OBBO based controller with SMES in terms of system oscillations, settling time and frequency deviation.

Optimal Allocation of Static Synchronous Series Compensator Controllers using Chemical Reaction Optimization for Reactive Power Dispatch

Static synchronous series compensator (SSSC) is one of the most effective flexible AC transmission systems (FACTS) devices used for enhancing power system security. In this paper, optimal location and sizing of SSSC are investigated for solving the optimal reactive power dispatch (ORPD) problem in order to minimize the active power loss in the transmission networks. A new and efficient chemical reaction optimization (CRO) is proposed to find the feasible optimal solution of the SSSC based optimal reactive power dispatch (ORPD) problem. The proposed approach is carried out on the standard IEEE 30 bus and IEEE 57 bus test systems. The optimization results obtained by the proposed CRO are analyzed and compared with the same obtained from genetic algorithm (GA), teaching learning based optimization (TLBO), quasi-oppositional TLBO (QOTLBO) and strength pareto evolutionary algorithm (SPEA). The results demonstrate the capabilities of the proposed approach to generate true and well-distributed optimal solutions.

Hybrid biogeography–based optimisation for optimal power flow incorporating FACTS devices

This paper presents hybrid biogeography–based optimisation (BBO) technique to solve optimal power flow (OPF) in power system incorporating flexible AC transmission systems (FACTS). The proposed hybrid method combines the BBO algorithm with the differential evolution (DE) to improve the performance of the conventional BBO and DE algorithms. Here, BBO is the main optimiser, while the DE is used to fine tune the solution of the BBO algorithm. Two types of FACTS devices namely, thyristor–controlled series capacitor (TCSC) and thyristor–controlled phase shifter (TCPS) are considered in this paper. The proposed method is implemented with MATLAB and tested on modified IEEE 30–bus system in five different cases. The simulation results show that the proposed HDE–BBO algorithm is effective, fast and accurate in finding the optimal parameter settings for FACTS devices to solve OPF problems. The proposed HDE–BBO method gives better solution quality compared to particle swarm optimisation with inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), differential evolution (DE) and conventional BBO. The simulation study also shows that FACTS devices are capable of providing an economically attractive solution to power system problems.

Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

In this paper, quasi-oppositional teaching-learning based optimization QOTLBO is introduced and successfully applied for solving an optimal power flow OPF problem in power system incorporating flexible AC transmission systems FACTS. The main drawback of the original teaching-learning based optimization TLBO is that it gives a local optimal solution rather than the near global optimal one in limited iteration cycles. In this paper, opposition based learning OBL concept is introduced to improve the convergence speed and simulation results of TLBO. The effectiveness of the proposed method implemented with MATLAB and tested on modified IEEE 30-bus system in four different cases. The simulation results show the effectiveness and accuracy of the proposed QOTLBO algorithm over other methods like conventional BBO and hybrid biogeography-based optimization HDE-BBO. This method gives better solution quality in finding the optimal parameter settings for FACTS devices to solve OPF problems. The simulation study also shows that using FACTS devices, it is possible to improve the quality of the electric power supply thereby providing an economically attractive solution to power system problems.

Optimal location of TCSC using hybrid DE/BBO algorithm

Obtaining optimal power flow solution is a strenuous task for any power system engineer. The inclusion of FACTS devices in the power system network adds to its complexity. This paper presents a novel algorithm named hybridization of DE with BBO (DE/BBO) for solving the optimal placement and parameter setting of thyristor controlled series compensator (TCSC) to achieve optimal power flow (OPF) of power system network. The proposed method is tested and validated for locating TCSC in IEEE-30 test system. Results show that the proposed method is good to select proper location of TCSC for secured OPF.

Optimal Power Flow Using Evolutionary Algorithms

Optimal Power Flow Using Evolutionary Algorithms provides emerging research exploring the theoretical and practical aspects of optimizing power system operation through advanced electronic power devices. Featuring coverage on a broad range of topics such as hybridization algorithm, power system modeling, and transmission systems, this book is ideally designed for engineers, power system developers, academicians, and researchers seeking current research on emerging techniques in achieving quality power under normal operating conditions.

Krill herd algorithm for optimal UPFC placement in transmission system

Evolutionary algorithms (EAs) are well-known optimisation approaches to deal with nonlinear and complex problems. However, most of these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a fast, efficient and relatively new krill herd algorithm (KHA) to find optimal location of unified power flow controller (UPFC) for solving the optimal power flow (OPF) problem taking nonlinearities of valve-point effects into consideration. The proposed algorithm is tested on standard IEEE 30-bus system incorporating single and multiple UPFC devices for two different load conditions. The simulation results of the proposed KHA method are compared with other well popular artificial intelligent techniques namely, particle swarm optimisation (PSO), differential evolution (DE), genetic algorithm (GA) and biogeography-based optimisation (BBO). The solutions obtained by the proposed KHA algorithm are quite encouraging and it is found that the proposed KHA-based approach is able to provide better solution than other evolutionary optimisation techniques in terms of cost, computation time and convergence.