Optimal Thyristor Controlled Series Compensator Research Articles

Multiobjective optimal TCSC placement using multiobjective grey wolf optimizer for power losses reduction

This study investigates the application of the multiobjective grey wolf optimizer (MOGWO) for optimal placement of thyristor-controlled series compensator (TCSC) to minimize power loss in power systems. Two conflicting objectives are considered: (1) minimizing real and reactive power loss, and (2) minimizing real power loss and TCSC capital cost. The Pareto-optimal method is employed to generate the Pareto front for these objectives. The fuzzy set technique is used to identify the optimal trade-off solution, while the technique for order preference by similarity to the ideal solution suggests multiple optimal solutions catering to diverse utility preferences. Simulations on an IEEE 30 bus test system demonstrate the effectiveness of TCSC placement for power loss minimization using MOGWO. The superiority of MOGWO is confirmed by comparing its results with those obtained from a multiobjective particle swarm optimization algorithm. These findings can assist power system utilities in identifying optimal TCSC locations to maximize their performance.

Optimal TCSC Allocation via Chaotic Immune Symbiotic Organisms Search for Voltage Profile Improvement

As the load demand in a power system increases, power system operators struggle to maintain the power system to be operated within its acceptable limits. If no mitigation actions are taken, a power system may suffer from voltage collapse, which in turn leads to blackout. Flexible AC Transmission System (FACTS) devices can be employed to help improve the voltage profile of the power system. This paper presents the implementation of Chaotic Immune Symbiotic Organism Search (CISOS) optimization technique to solve optimal Thyristor Controlled Series Compensator (TCSC) in a power system for voltage profile improvement. Validation process are conducted on IEEE 26-bus RTS resulting in the capability of CISOS in solving the allocation problem with a better voltage profile. Comparative studies conducted with respect to Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) has revealed the superiority of CISOS over PSO and EP in solving the optimal allocation problem by producing optimal solution with a better voltage profile. The results and information obtained from this study can help power system operator in terms of optimal compensation in power system as well as improving the operation of a power system.

An Effective Method for Maximizing Social Welfare in Electricity Market via Optimal TCSC Installation

The evolving electricity market has increased power demand and has brought many social benefits. Meanwhile, the transmission systems are not developed to the same extent because building new lines is difficult for environmental and political reasons. Hence, the systems are driven close to their limits resulting in congestions and critical situations endangering the system’s security. Due to this, the study of enhancing the transfer capability of existing power networks to satisfy the increased power demand, maximum social welfare, and ensure its secure operation has become one of the challenges the Independent System Operator faces in the electricity market. In order to solve this problem without building more transmission lines, the installation of FACTS devices can be a better alternative. Among FACTS devices, thyristor-controlled series compensator (TCSC) is one that can redistribute power flow in the network to improve the transfer capability of the existing system effectively. However, it is very difficult to implement. This paper presents the implementation of the Cuckoo Optimization Algorithm (COA) to solve the OPF problem which is formulated as a nonlinear optimization problem with equality and inequality constraints in a power system for social welfare maximization via the optimal installation of TCSC devices. As Cuckoo Search Algorithm (CSA), the COA also starts with an initial population-based metaheuristic optimization inspired by the nature of brood parasitism of some cuckoo species. However, unlike CSA, COA uses the cuckoo’s style for egg-laying to optimize the local search instead of using Lévy flights. This model is tested in IEEE 14 and IEEE 30 bus systems. Simulations results are compared with GA and GWO and show that the COA is one useful method for TCSC installation to maximize social welfare.

Hybrid technique for optimal location and cost sizing of thyristor controlled series compensator to upgrade voltage stability

In this study, a hybrid technique is employed for ameliorating the voltage stability of test system with thyristor controlled series compensator (TCSC) installation. Improved gravitational search algorithm is used to first set the optimal location of TCSC to generate optimal power flow solution under normal and contingency condition while firefly algorithm is utilised to improve voltage stability and TCSC cost sizing, respectively. The proposed technique is implemented in MATLAB working platform and the power flow security and voltage stability is evaluated with IEEE 6, 14 and 30 bus transmission systems. The optimal TCSC placement is governed by new proposed index of apparent power flow index. The performance of the proposed method is deduced to be superior when results are compared with traditional gravitational search algorithm (GSA) and other hybrid methods of fuzzy-GSA and radial basis function neural network techniques.

Oppositional biogeography-based optimisation applied to SMES and TCSC-based load frequency control with generation rate constraints and time delay

This article introduces biogeography-based optimisation (BBO) and oppositional BBO for solving load frequency control (LFC) in power system. Two widely employed test systems, viz. two-area multi-unit thermal-thermal and three-area thermal power plant, are considered for analysis. To make the study realistic, inherent power system nonlinearities like time delay, generation rate constraint and reheater are included. 2nd order Pade approximation is used to find linear model of time delay. To provide additional damping to frequency and tie-line power oscillations caused by sudden load perturbation, optimal thyristor controlled series compensator (TCSC) and superconducting magnetic energy storage (SMES) are design in coordination to LFC for the concerned power systems. An extensive comparative analysis is performed to established superiority of proposed method over other recently published algorithms. Simulation results show that coordinated LFC-TCSC-SMES controller yields greater dynamic performance compared to other. Finally, sensitivity analysis is performed to demonstrate the robustness of the proposed controller.

Oppositional biogeography-based optimisation applied to SMES and TCSC-based load frequency control with generation rate constraints and time delay

This article introduces biogeography-based optimisation (BBO) and oppositional BBO for solving load frequency control (LFC) in power system. Two widely employed test systems, viz. two-area multi-unit thermal-thermal and three-area thermal power plant, are considered for analysis. To make the study realistic, inherent power system nonlinearities like time delay, generation rate constraint and reheater are included. 2nd order Pade approximation is used to find linear model of time delay. To provide additional damping to frequency and tie-line power oscillations caused by sudden load perturbation, optimal thyristor controlled series compensator (TCSC) and superconducting magnetic energy storage (SMES) are design in coordination to LFC for the concerned power systems. An extensive comparative analysis is performed to established superiority of proposed method over other recently published algorithms. Simulation results show that coordinated LFC-TCSC-SMES controller yields greater dynamic performance compared to other. Finally, sensitivity analysis is performed to demonstrate the robustness of the proposed controller.

Multi‐objective optimal thyristor controlled series compensator installation strategy for transmission system loadability enhancement

Owing to the continuously increased electricity demands and power transactions, power systems are becoming more vulnerable to insecurity, generally incurred by overutilised transmission facilities or any contingency. In order for existing transmission networks to accommodate more power transfers with less network expansion cost, proper installation of thyristor controlled series compensator (TCSC) is validated to be one of the most ‘promising options’. The multi-objective optimal TCSC installation strategy proposed in the study first applies the performance index sensitivity factor technique to investigate which lines are most necessary for TCSC installation and with the lines specified for TCSC installation and the multi-objective function consisting of maximum system loadability and minimum TCSC installation cost, the problem to determine the capacity for each TCSC installation is then formulated as a multi-objective optimisation problem and solved by using the fitness sharing multi-objective particle swarm optimisation method. Finally, in the Pareto front set obtained, the solution with the TCSC installations that can make the power system provide the required loadability with biggest utilisation index value is recommended. The modified IEEE-14 buses, IEEE-118 buses systems and a practical power system are used to validate the performance of the proposed method.

A Particle Swarm Optimization based Approach for Power System Transient Stability Enhancement with TCSC

Power system engineers are currently facing challenges to increase the power transfer capabilities of existing transmission system. This led to the evolution of FACTS technology. FACTS controller helps in raising dynamic stability limit, provide better power flow control also provides improvement in asset utilization, system flexibility and system performance. Thyristor controlled series compensator (TCSC) is a series FACTS controller which can control the line impedance, improve network stability and damp the power system oscillations. This paper presents a procedure for modelling and optimal tuning the parameters of Thyristor controlled series compensator (TCSC) controller for the power system transient stability. For the simulation purpose, the model of Single Machine Infinite Bus (SMIB) power system with TCSC controller is developed in MATLAB. The design problem of TCSC controller is formulated as an optimization problem and particle swarm optimization (PSO) is applied to search for the optimal TCSC control parameters. The results obtained shows that the proposed TCSC controller is effective in damping small disturbance condition in the power system.

Modelling, simulation and optimal tuning of TCSC controller

A systematic procedure for modeling, simulation and optimal tuning the parameters of a Thyristor Controlled Series Compensator (TCSC) controller, for the power system stability enhancement is presented in this paper. For the simulation purpose, the model of singlemachine infinite bus (SMIB) power system with TCSC controller is developed in MATLAB/SIMULINK. The design problem of TCSC controller is formulated as an optimisation problem and genetic algorithm (GA) is employed to search for the optimal TCSC controller parameters. By minimizing a time-domain based objective function, in which the oscillatory rotor speed deviation of the generator is involved; stability performance of the power system is improved. The results obtained from simulations validate the effectiveness of proposed modelling and tuning approach for power system stability improvement. The simulation results also show that the proposed TCSC controller is effective in damping a range of small disturbance conditions in the power system. (Received in October 2006, accepted in February 2007. This paper was with the authors 1 month for 1 revision.)

OPTIMAL POWER FLOW MODEL WITH TCSC FOR PRACTICAL POWER NETWORKS

An optimal power flow model is developed and analyzed with TCSC (Thyristor Controlled Series Compensator) for practical power networks using Newton's optimization technique. The optimal generation schedules and corresponding transmission losses along with optimal TCSC setting parameters for different case studies are also reported. The performance of the proposed algorithm is tested for both IEEE-30 and IEEE-118 bus systems with multiple TCSC devices. Convergence of the proposed model is independent of number and initial condition of TCSCs. The authors also observe that the proposed algorithm can be applied to larger systems and does not suffer from computational difficulties.