One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design's central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke's law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

An inter-turn fault in a transformer can cause severe damage to transformer windings if not detected quickly. In this paper, the fault diagnosis is discussed by examining the no-load active power loss (NLAPL) and no-load reactive power (NLRP) of the transformer. Two indices, Total harmonic distortion of active power (THDAP) and Total harmonic distortion of reactive power (THDRP), based on odd harmonic components of NLAPL and NLRP, are proposed. To validate the obtained results, the acquired outcomes of tests on a physical device are compared with the results of the finite element analysis. To do this, three-dimensional model of a single-phase transformer is simulated in the transient mode using ANSYS MAXWELL 17.02. Results show that in the presence of an inter-turn short circuit fault on the transformer high voltage (HV) windings, the low voltage side current and NLAPL increase while the variation in NLRP is very small. In addition, from the transient analysis point of view, it becomes clear that THDAP and THDRP are reduced in the presence of an inter-turn fault.

In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors of animals, plants and other living things. Implementation of optimization algorithms on some objective functions has been successful and in others has led to failure. Improving the optimization process and adding modification phases to the optimization algorithms can lead to more acceptable and appropriate solution. In this paper, a new method called Dehghani method (DM) is introduced to improve optimization algorithms. DM effects on the location of the best member of the population using information of population location. In fact, DM shows that all members of a population, even the worst one, can contribute to the development of the population. DM has been mathematically modeled and its effect has been investigated on several optimization algorithms including: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), and grey wolf optimizer (GWO). In order to evaluate the ability of the proposed method to improve the performance of optimization algorithms, the mentioned algorithms have been implemented in both version of original and improved by DM on a set of twenty-three standard objective functions. The simulation results show that the modified optimization algorithms with DM provide more acceptable and competitive performance than the original versions in solving optimization problems.

Random based inventive algorithms are being widely used for optimization. An important category of these algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed algorithm for optimizing the orientation game is a series of optimization algorithms that are formed with the idea of an old game and search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the game referee's orders. The best position is achieved by the laws are there in this game .In this paper, the real version of the algorithm is presented. The results of optimization of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed algorithm over the genetic algorithm and the particle swarm optimization algorithm.

Purpose. In this paper, for simultaneous placement of distributed generation (DG) and capacitors, a new approach based on Spring Search Algorithm (SSA), is presented. This method is contained two stages using two sensitive index Sv and Ss. Sv and Ss are calculated according to nominal voltage and network losses. In the first stage, candidate buses are determined for installation DG and capacitors according to Sv and Ss, Then in the second stage, placement and sizing of distributed generation and capacitors are specified using SSA. The spring search algorithm is among the optimization algorithms developed by the idea of laws of nature and the search factors are a set of objects. The proposed algorithm is tested on 33-bus and 69-bus radial distribution networks. The test results indicate good performance of the proposed method.

Fossil fuels are known as the main energy source in the world. Factors such as increasing power demand, shortage of fossil fuel resources, and shortage of production capacity in industrialized countries, greenhouse gas emissions and climate change faced with problems in power production. Efficient use of energy storage systems such as batteries is considered as an adequate solution to avoid the above problems. In addition, the energy storage provides many advantages such as stability in unstable or unbalanced production load, improve voltage profile, improve power quality, economic benefits and improve the reliability of power system. In this article, effectively on losses, voltage profile, and network load is controlled using energy storage units (Batteries) and development of Optimal Power Flow (OPF). The proposed method is implemented on a typical radial distribution network using software Digsilent® Power Factory. The test result is provided.

The inter-turn fault is a common internal fault in the windings of transformers. This fault can cause the increase of transformer leakage flux. In this paper, the leakage inductance caused by the inter turn-fault is calculated by modeling of a single-phase transformer using finite element method. Finite element model of the transformer is simulated considering Non-linear magnetic characteristic of the core using ANSYS Electromagnetics Suite 17.2 software. in order to calculate the leakage inductance, two and three-dimensional modelings are used in the magnetostatic states. Data obtained from the finite element method were compared with data obtained from analytical method. The results show the high accuracy of finite element method in short-circuit inter-turn fault detection in the transformer.