Enhanced Symbiotic Organisms Search and Hydrological Cycle Algorithms for Real Power Loss Diminution and Voltage Stability Enhancement

In this chapter, enhanced symbiotic organisms search (ESOS) algorithm and hydrological cycle (HC) algorithm are projected to solve factual power loss lessening problem. Symbiotic search algorithm is based on the actions between two different organisms in the ecosystem: mutualism, commensalism, and parasitism. Exploration procedure has been initiated arbitrarily, and each organism indicates a solution with fitness value. Quasi-oppositional-based learning and chaotic local search have been applied to augment the performance of the algorithm. In this work, hydrological cycle (HC) algorithm has been utilized to solve the optimal reactive power problem. It imitates the circulation of water form land to sky and vice versa. Only definite number of water droplets is chosen for evaporation, and it is done through roulette-wheel selection method. In the condensation stage, water drops move closer, combine, and also collusion occurs as the temperature decreases.

Real Power Loss Reduction by Cinnamon ibon Search Optimization Algorithm

In this paper Cinnamon ibon Search Optimization Algorithm (CSOA) is used for solving the power loss lessening problem. Key objectives of the paper are Real power Loss reduction, Voltage stability enhancement and minimization of Voltage deviation. Searching and scavenging behavior of Cinnamon ibon has been imitated to model the algorithm. Cinnamon ibon birds which are in supremacy of the group are trustworthy to be hunted by predators and dependably attempt to achieve a improved position and the Cinnamon ibon ones that are positioned in the inner of the population, drive adjacent to the nearer populations to dodge the threat of being confronted. The systematic model of the Cinnamon ibon search Algorithm originates with an arbitrary individual of Cinnamon ibon. The Cinnamon ibon search algorithm entities show the position of the Cinnamon ibon. Besides, the Cinnamon ibon bird is supple in using the cooperating plans and it alternates between the fabricator and the cadger. Successively the Cinnamon ibon identifies the predator position; then they charm the others by tweeting signs. The cadgers would be focussed to the imperilled regions by fabricators once the fear cost is more than the defence threshold. Likewise, the subterfuge of both the cadger and the fabricator is commonly used by Cinnamon ibon. The dispersion of the Cinnamon ibon location in the solution area is capricious. An impulsive drive approach was applied when dispossession of any adjacent Cinnamon ibon in the purlieu of the present population. This style diminishes the convergence tendency and decreases the convergence inexorableness grounded on the controlled sum of iterations. Authenticity of the Cinnamon ibon Search Optimization Algorithm (CSOA) is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Proportion of actual power loss lessening is amplified.

Adaptive Dragonfly Optimization Based Placement of Capacitor Banks for Voltage Stability Enhancement in Distribution Networks

The present day ever escalating power demand moves the operating point of the distribution networks in the vicinity of the voltage stability boundaries. Any small reactive power unbalance between the generation and demand may trigger voltage instability, which makes the voltage magnitude to fall slowly until a rapid change occurs. This paper presents an adaptive dragonfly optimization based method for placement of shunt capacitor banks with a view of improving the voltage stability besides enhancing the voltage profile and lowering the network losses. Simulation results on 33 and 69-node distribution networks exhibit the greater performances of the suggested algorithm.

A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms

This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

Voltage stability enhancement for large scale squirrel cage induction generator based wind turbine using STATCOM

<p><span lang="EN-US">A stable operation of wind turbines connected to the grid is an essential requirement to ensure the reliability and stability of the power system. To achieve such operational objective, installing static synchronous compensator static synchronous compensator (STATCOM) as a main compensation device guarantees the voltage stability enhancement of the wind farm connected to distribution network at different operating scenarios. STATCOM either supplies or absorbs reactive power in order to ensure the voltage profile within the standard-margins and to avoid turbine tripping, accordingly. This paper present new study that investigates the most suitable-location to install STATCOM in a distribution system connected wind farm to maintain the voltage-levels within the stability margins. For a large-scale squirrel cage induction generator squirrel-cage induction generator (SCIG-based) wind turbine system, the impact of STATCOM installation was tested in different places and voltage-levels in the distribution system. The proposed method effectiveness in enhancing the voltage profile and balancing the reactive power is validated, the results were repeated for different scenarios of expected contingencies. The voltage profile, power flow, and reactive power balance of the distribution system are observed using MATLAB/Simulink software. </span></p>

Self-Adaptive Firefly-Algorithm-Based Unified Power Flow Controller Placement with Single Objectives

The selection of positions for unified power flow controller (UPFC) placement in transmission network is an essential factor, which aids in operating the system in a more reliable and secured manner. This paper focuses on strengthening the power system performance through UPFC placement employing self-adaptive firefly algorithm (SAFA), which selects the best positions along with parameters for UPFC placement. Three single objectives of real power loss reduction, voltage profile improvement, and voltage stability enhancement are considered in this work. IEEE 14, 30, and 57 test systems are selected to accomplish the simulations and to reveal the efficacy of the proposed SAFA approach; besides, solutions are compared with two other algorithms solutions of honey bee algorithm (HBA) and bacterial foraging algorithm (BFA). The proposed SAFA contributes real power loss reduction, voltage profile improvement, and voltage stability enhancement by optimally choosing the placement for UPFC.