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Protection relays in a power system makeup to pairs, one as main protection relay and the other as a backup protection relay. Directional over current relays (DOCR) have been using for backup protection mainly in distribution system. Coordination of these DOCRs is an optimization problem that is being solved using various types of optimizing algorithms by the researchers in the field. This paper attempts this problem in different direction which analyzes different inverse characteristics of DOCR and their sensitivities to different faults. Optimal values for time dial and plug settings for the DOCRs are obtained by using a recently developed algorithm: Opposition based chaotic differential evolution algorithms. IEEE 4-bus system has been taken as a test model and the algorithms are implemented in it for different types of characteristics based on the degree of inversion i.e., normally inverse, very inverse, extremely inverse and long-time inverse characteristics.

Coordination of directional over current relays (DOCR) has been considered as an optimization problem and has been solved using different types of optimization techniques by the developers. A wide variety of algorithms have been proposed over the past few decades for dealing with such problem. However considering its practical significance, there is always a scope for improvement in this area to get better results. In the present study, a set of two algorithms named opposition based chaotic differential evolution (OCDE1 and OCDE2) has been developed and applied to solve this problem. Both variants use the concept of opposition based learning and chaotic scale factor for obtaining an optimum solution. The proposed method is implemented in four test cases. The results are compared with previously proposed algorithms available in the literature and it was observed that the proposed schemes are quite competent for dealing with such problems.

This article deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machines in the textile industry, using three newly developed versions of differential evolution DE algorithms called modified DE versions CMDE, GMDE, and LMDE. Efficiency, which decides the operating or running cost of the motor industry, is considered as the objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance-related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries, and less costly to operate, certain constraints are modified in accordance with the demands of the spinning application. Comparison of the optimum designs with the industrial existing motor reveals that the motor designed by the proposed algorithms consumes less power input.

Induction motor is the most frequently used electric machine in industrial and commercial applications because of its well-known advantages, including robustness, low price, maintenance free, and line start. The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and its in situ efficiency determination. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. In this present study, particle swarm optimization, differential evolution, and some of their recent variants are used for in situ efficiency determination of induction motor (5 hp) without performing no-load test. Detailed information about the methods for efficiency measurement is given. Results are compared with genetic algorithm and a physical efficiency measurement method, called torque-gauge method. The performances in terms of objective function (error in the efficiency) and convergence time prove the effectiveness of optimization algorithms used in this article.

Stochastic (or probabilistic) programming (SP) is an optimization technique in which the constraints and/or the objective function of an optimization problem contain random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, two modified differential evolution (DE) algorithms namely, LDE1 and LDE2 are used for solving SP problems. Two models of SP problems are considered; Stochastic Fractional Programming Problems and Multiobjective Stochastic Linear Programming Problems. The numerical results obtained by the LDE algorithms are compared with the results of basic DE, basic particle swarm optimization (PSO) and the available results from where it is observed that the LDE algorithms significantly improve the quality of solution of the considered problem in comparison with the quoted results in the literature.

Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or more) techniques in a judicious manner such that the resulting algorithm contains the positive features of both (or all) the algorithms. Depending on the algorithm/s used we made three classifications as (i) Hybridization of PSO and genetic algorithms (ii) Hybridization of PSO with differential evolution and (iii) Hybridization of PSO with other techniques. Where, other techniques include various local and global search methods. Besides giving the review we also show a comparison of three hybrid PSO algorithms; hybrid differential evolution particle swarm optimization (DE-PSO), adaptive mutation particle swarm optimization (AMPSO) and hybrid genetic algorithm particle swarm optimization (GA-PSO) on a test suite of nine conventional benchmark problems.

This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems.

Optimization of directional over-current relay (DOCR) settings is an important problem in electrical engineering. The optimization model of the problem turns out to be non-linear and highly constrained in which two settings namely time dial setting (TDS) and plug setting (PS) of each relay are considered as decision variables; the sum of the operating times of all the primary relays, which are expected to operate in order to clear the faults of their corresponding zones, is considered as an objective function. In the present study, three models are considered namely IEEE 3-bus model, IEEE 4-bus model and IEEE 6-bus model. To solve the problem, we have applied five newly developed versions of differential evolution (DE) called modified DE versions (MDE1, MDE2, MDE3, MDE4, and MDE5). The results are compared with the classical DE algorithm and with five more algorithms available in the literature; the numerical results show that the modified DE algorithms outperforms or perform at par with the other algorithms.

This article presents particle swarm optimization (PSO)-based optimal gain tuning of proportional integral (PI) speed controller in an induction motor (IM) drive (30 hp) with mine hoist load diagram. Optimization considers the load and speed variations, and provides appropriate gains to the speed controller to obtain good dynamic performance of the motor. IM performance is checked with the optimal gains through the simulation studies in MATLAB/SIMULINK environment. Results are compared with hand tuning (fixed gains) and fuzzy logic (FL) speed controller. Hybrid of FL and PSO-based PI controller for the speed control of given motor is also performed to eliminate the drawbacks of PI controller (overshoot and undershoot) and FL controller (steady-state error). From the simulation studies, hybrid controller produces better performance in terms of rise time, overshoot and settling time.

Differential evolution is a novel evolutionary approach capable of handling non-differentiable, nonlinear and multimodal objective functions. It has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. In the present study we propose five new mutation schemes for the basic DE algorithm. The corresponding versions are termed as MDE1, MDE2, MDE3, MDE4 and MDE5. These new schemes make use of the absolute weighted difference between the two points and instead of using a fixed scaling factor F, use a scaling factor following the Laplace distribution. The performance of the proposed schemes is validated empirically on a suit of ten benchmark problems having box constraints. Numerical analysis of results shows that the proposed schemes improves the convergence rate of the DE algorithm and also maintains the quality of solution. Efficiency of the proposed schemes is further validated by applying it to a real life electrical engineering problem dealing with the optimization of directional over-current relay settings. It is a highly constrained nonlinear optimization problem. A constraint handling mechanism based on repair methods is used for handling the constraints. Once again the simulation results show the compatibility of the proposed schemes for solving the real life problem.

Field Oriented Control (FOC) of induction motor drive is more popular in process and manufacturing industries as it controls the motor as desired. To perform FOC, Variable Voltage Variable Frequency (VVVF) operation of an inverter, connected in the stator or rotor circuit (in case of wound rotor) of the machine, is mandatory. For VVVF control, the rotor speed and stator currents have to be measured by sensors and the measured signals have to be fed back to the controllers. The proper operation of the control system depends on the feedback signals from sensors. When sensors are undergone a fault, the stability of control system, may be designed for an industrial process, is disturbed and hence the reliability of the system is decreased. The fault in speed sensors may be open circuit fault, omission fault, gain, bias, and saturation. In this paper three different speed controllers like PI Controller, Fuzzy Logic Controller (FLC), Fuzzy Pre compensated Proportional Integral Controllers (FPPI) are used in the speed control loops of FOC and analyze their performances under sensor faults. A 30-hp induction motor drive is considered with mine-hoist load diagram as case study.

Induction motor (IM) is considered as a major consumer of electrical power in any process industries. Efficiency and power factor of such machines are considerably reduced at light loads. Enhancement of cost effective energy conservation (EC) technology plays a significant role in these machines operating at light loads. In-situ efficiency determination is essential before taking any decision on implementation of EC. This paper determines the efficiency of IM without performing no-load test, which is not easily possible for in-service motors, by using evolutionary algorithms. Benefits of Star-delta (Υ-Δ) based EC scheme are estimated.

This chapter presents application of two popular Nature Inspired Algorithms (NIA); Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms for solving the optimization problems that arise in the field of electrical engineering. The main focus is on efficient utilization of electrical energy and the protection of transmission lines, the most important fields of optimization in electrical engineering having nonconvex characteristics with several equality and inequality constraints. The PSO and DE variants are applied to various electrical engineering problems including over-current relay settings in transmission lines, in-situ parameter estimation of electric motors, design and control of induction motors (IM) serving to process industries and proportional-integral (PI) controller tuning in variable speed drives.

In this paper, three modified Differential Evolution algorithms based on local neighborhood search (LNS) is used to calculate the optimal relay settings of directional overcurrent relays in power systems. The process of LNS is done by exploring the vicinity of the particle having the best fitness function value (global best particle) with the help of mutation operators based on different probability distributions viz. Laplace, Cauchy and Gaussian. The proposed schemes named as LMDE, CMDE and GMDE help in enhancing the exploration and exploitation capabilities of DE without imposing any serious threat on the number of function evaluations. Two models are considered namely IEEE 3-bus model and IEEE 4-bus model to check the efficiency of the algorithms proposed in this paper. The results obtained by all the DE algorithms are compared with the basic Differential Evolution (DE) and eleven other algorithms available in the literature; the numerical results show that the proposed algorithms are able to provide superior results in comparison to the other algorithms.

A modified differential evolution (DE) algorithm based on opposition based learning and chaotic sequence named Opposition based Chaotic Differential Evolution (OCDE) is proposed. The proposed OCDE algorithm is different from basic DE in two aspects. First is the generation of initial population, which follows Opposition Based Learning (OBL) rules; and the second is: dynamic adaption of scaling factor F using chaotic sequence. The numerical results obtained by OCDE when compared with the results obtained by DE and ODE (opposition based DE) algorithms on eighteen benchmark function demonstrate that the OCDE is able to find a better solution while maintaining a reasonable convergence rate.

Due to robustness, reliability, low price and maintenance free, induction motors (IMs) are used in most of the industrial applications. The influence of these motors (in terms of energy consumption) in energy intensive industries is significant in total input cost. The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and its in-situ efficiency determination. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. Here, Particle Swarm Optimization is used for in-situ efficiency determination of induction motor (5 hp) without performing no-load test. Results are compared with physical efficiency measurement method. The error between estimated and actual efficiencies is found for different objective functions and for different standards.

This paper illustrates the importance of controllers on energy and cost saving opportunity of induction motor for mine hoist load diagram. The economics of a 100 HP induction motor is investigated with two topologies namely constant Volt/frequency (V/f) controller and Model Based Controller (MBC) using Differential evolution (DE) algorithm in steady-state conditions. In this study, the field excitation in a machine has been considered to adjust to give minimum stator current, minimum operating cost and maximum efficiency for different load/speed. The MBC controller improves the economics in terms of operating cost (energy cost and demand charge cost) and the simulation results show that the slip speed in the most economic motor will be adjusted according to load and speed, particularly at light load.

Nature Inspired Optimization Algorithms (NIOA) are inspired by biological and sociological phenomena and can take care of optimality on rough, discontinuous and multimodal surfaces. During the last few decades, these algorithms have been successfully applied for solving numerical bench mark problems and real life problems. This paper presents the application of two popular NIOA, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving multi-objective stochastic programming problems. The numerical results obtained by PSO and DE are compared with the available results from where it is observed that the PSO and DE algorithms significantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature.

This paper deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machine in textile industry, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Efficiency, which decides the operating or running cost of the motor (industry), is considered as objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries and less operating cost, certain constraints are modified in accordance with the demands in spinning application. Comparison of the optimum designs with the industrial (existing) motor reveals that the motor designed for textile load diagram consumes less power input. Economical analysis is also given.

Stochastic (or probabilistic) programming is an optimization technique in which the constraints and/or the objective function of an optimization problem contains random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, Differential Evolution (DE) and its two recent variants LDE1 and LDE2 are presented for solving multi objective linear stochastic programming (MOSLP) problems, having several conflicting objectives. The numerical results obtained by DE and its variants are compared with the available results from where it is observed that the DE and its variants significantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature.

The weapon target assignment problem can be modeled as an optimization problem in which the objective is to assign weapons to target in order to maximize the optimum target damage value. The mathematical model of the problem is subject to various constraints depending on the availability of weapons The objective function of the problem is non linear and the constraints are linear in nature. Also, the variables are subjected to integer restrictions. In this paper Quantum Particle Swarm Optimization (QPSO) algorithm and its two variants are used to solve a hypothetical but realistic weapon assignment problem. The numerical results obtained by the QPSO algorithms are compared with the results given in the literature.

This paper presents an application of Differential Evolution (DE) to determine optimal crop plan for command area of Pamba-Achankovil-Vaippar (PAV) link project, so as to maximize the net irrigation benefit. The mathematical model of the problem is linear in nature subject to various constraints due to availability of total land area, water, fertilizers, seeds and manure, etc. Numerical results show that DE gives a better performance in comparison to the usual software tools used for solving such problems.

Differential Evolution (DE) is a simple and efficient scheme for global optimization over continuous spaces. DE is generally considered as a reliable, accurate, robust and fast optimization techniques. It outperforms many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a new Differential Evolution algorithm based on Adaptive Control parameters (ACDE) is introduced. The performance of ACDE algorithm is investigated with ten standard benchmark problems and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical results show that the ACDE algorithm outperforms the classical DE in terms of all considered performance measures.

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid of Differential Evolution (DE) and Evolutionary Programming (EP). Based on the generation of initial population, three versions are proposed. Besides using the uniform distribution (U-MDE), the Gaussian distribution (G-MDE) and Sobol sequence (S-MDE) are also used for generating the initial population. Empirical results show that the proposed versions are quite competent for solving the considered test functions.

In this paper we present an application of two Computational Intelligence Algorithms, namely Particle Swarm Optimization (PSO) Algorithm and Differential Evolution (DE) for finding and optimal solution to two optimization problems that occur in a paper industry. The first problem deals with the economic optimization of a hypothetical but realistic Kraft pulping process and in the second problem we have considered the optimization of Boiler load allocation problem. Both the problems form an integral part of paper making process. The simulation results show the efficiency and time effectiveness of DE and PSO.