A Chaotic Particle Swarm Optimization (CPSO) Algorithm for Solving Optimal Reactive Power Dispatch Problem

This paper presents a chaotic particle swarm algorithm for solving the multi-objective reactive power dispatch problem. To deal with reactive power optimization problem, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively.  In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and simulation results show that (CPSO)   is more efficient than other algorithms in reducing the real power loss and maximization of voltage stability index. Keywords:chaotic particle swarm optimization, Optimization, Swarm Intelligence, optimal reactive power, Transmission loss

Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm

In this paper, a novel improved Stochastic Fractal Search optimization algorithm (ISFSOA) is proposed for finding effective solutions of a complex optimal reactive power dispatch (ORPD) problem with consideration of all constraints in transmission power network. Three different objectives consisting of total power loss (TPL), total voltage deviation (TVD) and voltage stabilization enhancement index are independently optimized by running the proposed ISFSOA and standard Stochastic Fractal Search optimization algorithm (SFSOA). The potential search of the proposed ISFSOA can be highly improved since diffusion process of SFSOA is modified. Compared to SFSOA, the proposed method can explore large search zones and exploit local search zones effectively based on the comparison of solution quality. One standard IEEE 30-bus system with three study cases is employed for testing the proposed method and compared to other so far applied methods. For each study case, the proposed method together with SFSOA are run fifty run and three main results consisting of the best, mean and standard deviation fitness function are compared. The indication is that the proposed method can find more promising solutions for the three cases and its search ability is always more stable than those of SFSOA. The comparison with other methods also give the same evaluation that the proposed method can be superior to almost all compared methods. As a result, it can conclude that the proposed modification is really appropriate for SFSOA in dealing with ORPD problem and the method can be used for other engineering optimization problems

Enriched Particle Swarm Optimization for Solving Optimal Reactive Power Dispatch Problem

In this paper, a different approach, Enriched particle Swarm optimization (EPSO) Algorithm for solving optimal reactive power dispatch problem has been presented. Particle swarm optimization is affected by early convergence, no assurance in finding optimal solution. This paper proposes EPSO using multiple sub swarm PSO in blend with multi exploration space algorithm. The particles are alienated into equal parts and arrayed into the number of sub swarms available. Multi-exploration space algorithm is used to obtain an optimum solution for each sub swarm and these solutions are then arrayed yet into a new swarm to obtain the best of all the solution. The proposed EPSO algorithm has been tested on standard IEEE 30 bus test system and simulation results show the commendable performance of the proposed algorithm in reducing the real power loss. Keywords:Optimal Reactive Power, Transmission loss, Enriched particle Swarm optimization, Multi-exploratio

Adopting Scenario-Based approach to solve optimal reactive power Dispatch problem with integration of wind and solar energy using improved Marine predator algorithm

The penetration of renewable energy resources into electric power networks has been increased considerably to reduce the dependence of conventional energy resources, reducing the generation cost and greenhouse emissions. The wind and photovoltaic (PV) based systems are the most applied technologies in electrical systems compared to other technologies of renewable energy resources. However, there are some complications and challenges to incorporating these resources due to their stochastic nature, intermittency, and variability of output powers. Therefore, solving the optimal reactive power dispatch (ORPD) problem with considering the uncertainties of renewable energy resources is a challenging task. Application of the Marine Predators Algorithm (MPA) for solving complex multimodal and non-linear problems such as ORPD under system uncertainties may cause entrapment into local optima and suffer from stagnation. The aim of this paper is to solve the ORPD problem under deterministic and probabilistic states of the system using an improved marine predator algorithm (IMPA). The IMPA is based on enhancing the exploitation phase of the conventional MPA. The proposed enhancement is based on updating the locations of the populations in spiral orientation around the sorted populations in the first iteration process, while in the final stage, the locations of the populations are updated their locations in adaptive steps closed to the best population only. The scenario-based approach is utilized for uncertainties representation where a set of scenarios are generated with the combination of uncertainties the load demands and power of the renewable resources. The proposed algorithm is validated and tested on the IEEE 30-bus system as well as the captured results are compared with those outcomes from the state-of-the-art algorithms. A computational study shows the superiority of the proposed algorithm over the other reported algorithms

Reduction of Real Power Loss by using Enhanced Particle Swarm Optimization Algorithm

In this paper, an Enhancedparticle swarm optimization algorithm (EPSO) has been proposed to solve the reactive power problem. Particle Swarm Optimization (PSO) is swarm intelligence based exploration and optimization algorithm which is used to solve global optimization problems. But due to deficiency of population diversity and early convergence it is often stuck into local optima. We can upsurge diversity and avoid premature convergence by using evolutionary operators in PSO. In this paper the intermingling crossover operator is used to upsurge the exploration capability of the swarm in the exploration space .Particle Swarm Optimization uses this crossover method to converge optimum solution in quick manner .Thus the intermingling crossover operator is united with particle swarm optimization to augment the performance and possess the diversity which guides the particles to the global optimum powerfully. The proposedEnhanced particle swarm optimization algorithm (EPSO) has been tested in standard IEEE 30, 57,118 bus test systems and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and control variables are well within the limits. Keywords: Optimal Reactive Power, Transmission loss, intermingling crossover operato

Real Power Loss Reduction and Voltage Stability Enhancement by Stock Exchange, Product Demand-Availability, Affluent and Penurious Algorithms

In this paper, the Stock Exchange Algorithm (SEA), the Product Demand-Availability (PDA) algorithm, and the Affluent and Penurious (AP) algorithm are proposed to solve the power loss reduction problem. In the SEA approach, selling and buying shares in the stock exchange was imitated to design the algorithm. Stockholders are classified as Privileged, Average or Weak based on their fitness value. The PDA optimization algorithm is based on the consumer demand and availability of a product in the market. The Affluent and Penurious algorithm mimics the social behavior of people. The gap parameter (G) is defined to indicate the growing gap between affluent and penurious people when affluent people increase their wealth. The proposed Stock Exchange Algorithm, Product Demand-Availability optimization algorithm and the Affluent and Penurious optimization algorithm were tested in the IEEE 30 bus system. Real power loss minimization, voltage deviation minimization, and voltage stability index enhancement were successfully attained

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed

Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

In this paper a Progressive particle swarm optimization algorithm (PPS) is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits