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Directional Overcurrent Relay (DOCR) coordination computation allowing for desired and high level accuracy in interconnected power systems is very difficult and is a highly constraint oriented optimization problem. This paper aims to study the effectiveness of a newly reported optimization technique, Teaching Learning Based Optimization (TLBO), in protective relay coordination comparing with a widely used optimization technique, Particle Swarm Optimization (PSO).

DOCR coordination in electric power systems is considered as an optimization problem by formulating objective function and specifying problem constraints. Optimum values of the DOCR adjustment parameters (Time Dial Setting and Plug Setting) in terms of reliable coordination margin and operating times of relays are computed by both the algorithms, TLBO and PSO. Optimal coordination is verified in three test bus systems: IEEE 6-bus, WSCC 9-bus and IEEE 14-bus systems.

A comparison between the numerical results of using both the algorithms indicates that the TLBO gives better results in terms of the total operating times of relays and Coordination Time Interval (CTI).

This paper represents the performance of a newly reported optimization technique, TLBO which is till now unpopular to protection engineers to be applied in protective relay coordination applications. The technique provides better performance in comparison to the widely applied technique, PSO. It is expected that TLBO would facilitate protection engineers to decide the optimum and appropriate settings of the relays for leading exact relays coordination.

Distribution network protection is a complicated problem and mal-operation of the protective relays due to false settings make the operation of the network unreliable. Besides, obtaining proper settings could be very complicated. This paper aims to discuss an innovative evolutionary Lightning Flash Algorithm (LFA) which is developed for solving the relay coordination problems in distribution networks. The proposed method is inspired from the movements of cloud to ground lightning strikes in a thunderstorm phenomenon. LFA is applied on three case study systems including ring, interconnected and radial distribution networks. The power flow analysis is performed in Digsilent Power Factory software; then the collected data are sent to MATLAB software for optimization process. The proposed algorithm provides optimum time multiplier setting and plug setting of all digital overcurrent relays in each system. The results are compared with other methods such as particle swarm optimization and genetic algorithm. The result comparisons demonstrate that the proposed LFA can successfully obtain proper relay settings in distribution networks with faster speed of convergence and lower total operation time of relays. Also, it shows the superiority and effectiveness of this method against other algorithms.

A novel LFA is designed based on the movements of cloud to ground lightning strikes in a thunderstorm. This method is used to optimally adjust the time multiplier setting and plug setting of the relays in distribution system to provide a proper coordination scheme.

The proposed algorithm was tested on three case study systems, and the results were compared with other methods. The results confirmed that the proposed method could optimally adjust the relay settings in the electric distribution system to provide a proper protection scheme.

The practical implications can be conducted on distribution networks. The studies provided in this paper approve the practical application of the proposed method in providing proper relay protection in real power system.

This paper proposes a new evolutionary method derived from the movements of cloud to ground lightning strikes in thunderstorm. The proposed method can be used as an optimization toolbox to solve complex optimization problems in practical engineering systems.

The purpose of this paper is to estimate that the start-up current parameters are stochastic or not. Electronic equipment in luminaries significantly improves their luminous efficiency, thereby increasing the energy efficiency of lighting installations. However, the use of electronics [e.g. electronic ballasts for discharge lamps or power supply units for light-emitting diode (LED) luminaries] may also cause some negative effects in lighting installations. One of such effects is large inrush current, which can greatly exceed the admissible line load and trigger the overcurrent protective devices.

The paper presents results of laboratory tests together with their statistical analysis of the inrush currents of lighting luminaires. Three road luminaires build in different technologies of similar power have been selected for the study. The theoretical distributions described by the analytical formulas matched the empirical distributions by using the MATLAB’ Statistical Toolbox.

As parameters that characterize short-time overcurrent at start-up are the maximum value of overcurrent amplitude in start-up moment (I_PIC), the duration of overcurrent in start-up moment (t_PIC) and melting integral MI. The aim of this statistical analysis of the selected parameter is to provide an overcurrent mathematical description allowing to estimate the probability of occurrence of values. For lighting luminaire fitted with magnetic ballasts, the parameters analyzed will randomly vary with the moment of power on. For electronic ballasts, the occurrence of this phenomenon depends on the adopted construction solution.

This will allow, for example, to estimate the probability of activation of protection device by comparing the value of the inrush current Joule’s integral MI with its value for the analyzed protection device. The proposed method may be useful for checking the selectivity of the protection devices in the lighting system.

The study enables application of a probabilistic model for analysis of inrush currents of lighting luminaire and predicting the possible consequences of their occurrence.

The purpose of this paper is to disseminate the lessons learned from the successful deployment of a wave energy converter (WEC) and accelerate growth in the field of ocean energy.

A thorough, well structured, documented, industrial approach was taken to the deployment because of the depth and scale of the task required. This approach is shown throughout the paper, which reflects the importance of a comprehensive project plan in success as well as failure.

The findings demonstrate the viability of the use of off shore WEC to generate electricity and that such a project can be completed on time and on budget.

The research implications of the paper include the importance of an enhanced, integrated supervisory system control in terms of efficiency, operation and maintenance, and long-term viability of WECs. This paper can be used to help guide the direction of further research in similar areas.

The practical implications include proof that WEC deployments can be carried out both on time and under budget. It highlights much of the practical data collected throughout the course of the project and presents it so that it might be used as a guide for future projects.

At the time of this paper, successful deployment of off shore WECs has been a rare accomplishment. Because the project was publicly funded, the data collected during this project, both technical and practical, is freely available.

The paper aims to present an application of teaching learning-based optimization (TLBO) algorithm and static Var compensator (SVC) to improve the steady state and dynamic performance of self-excited induction generators (SEIG).

The TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with different types of prime mover. For a constant speed prime mover, the TLBO algorithm attains the optimal capacitance to have rated load voltage at different loading conditions. In the case of variable speed prime mover, the TLBO methodology is used to obtain the optimal capacitance and prime mover speed to have rated load voltage and frequency. The SVC of fixed capacitor and controlled reactor is used to have a fine tune in capacitance value and control the reactive power. The parameters of SVC are obtained using the TLBO algorithm.

The whole system of three-phase induction generator and SVC are established under MatLab/Simulink environment. The performance of the SEIG is demonstrated on two different ratings (i.e. 7.5 kW and 1.5 kW) using the TLBO algorithm and SVC. An experimental setup is built-up using a 1.5 kW three-phase induction machine to confirm the theoretical analysis. The TLBO results are matched with other meta heuristic optimization techniques.

The paper presents an application of the meta-heuristic algorithms and SVC to analysis the steady state and dynamic performance of SEIG with optimal performance.

The purpose of this paper is to present a new pattern recognition‐based algorithm to detect high‐impedance faults (HIFs), including only with broken conductor and arcs, in distribution networks.

In the proposed method, using discrete wavelet transform, the time‐frequency‐based features of the current waveform are calculated. Then, to extract the best feature set of the generated time‐frequency features, principle components analysis (PCA) is applied and finally support vector machines (SVM) is used as a classifier to distinguish between the HIFs, including only with broken conductor and arcs, and other similar phenomena such as capacitor banks switching, no load transformer switching, load switching, insulator leakage current and harmonic loads.

The experimental results have shown that using SVM with PCA as the feature extraction method and radial basis function (RBF) as the kernel function has acceptable security and dependability performances in distinguishing HIFs, including only with broken conductor and arcs, from other similar phenomena and is superior to the Bayes and multi‐layer perceptron neural network classifiers.

Using new combination of time‐frequency‐based features with SVM provides a new algorithm to detect HIFs, including only with broken conductor and arcs, that has acceptable security and dependability.

Optimum utilization of investments has always been considered one of the most crucial aspects of capital markets. Investment into various securities is the subject of portfolio optimization intent to maximize return at minimum risk. In this series, a population-based evolutionary approach, stochastic fractal search (SFS), is derived from the natural growth phenomenon. This study aims to develop portfolio selection model using SFS approach to construct an efficient portfolio by optimizing the Sharpe ratio with risk budgeting constraints.

This paper proposes a constrained portfolio optimization model using the SFS approach with risk-budgeting constraints. SFS is an evolutionary method inspired by the natural growth process which has been modeled using the fractal theory. Experimental analysis has been conducted to determine the effectiveness of the proposed model by making comparisons with state-of-the-art from domain such as genetic algorithm, particle swarm optimization, simulated annealing and differential evolution. The real datasets of the Indian stock exchanges and datasets of global stock exchanges such as Nikkei 225, DAX 100, FTSE 100, Hang Seng31 and S&P 100 have been taken in the study.

The study confirms the better performance of the SFS model among its peers. Also, statistical analysis has been done using SPSS 20 to confirm the hypothesis developed in the experimental analysis.

In the recent past, researchers have already proposed a significant number of models to solve portfolio selection problems using the meta-heuristic approach. However, this is the first attempt to apply the SFS optimization approach to the problem.

Line–cell conversion and rotation of operators between cells are common in lean production systems. Thus, the purpose of this study is to provide an integrated look at these two practices through integrating job rotation scheduling and line-cell conversion problems, as well as investigating the effect of rotation frequency on flow time of a Seru system.

First, a nonlinear integer programming model of job rotation scheduling problem and line–cell conversion problem (Seru-JRSP) was presented. Then, because Seru-JRSP is NP-hard, an efficient and effective invasive weed optimization (IWO) algorithm was developed. Exploration process of IWO was enhanced by enforcing two shake mechanisms.

Computations of various sample problems showed shorter flow time and less number of assigned operators in a Seru system scheduled through job rotation. Also, nonlinear behavior of flow time versus number of rotation periods was shown. It was demonstrated that, setting number of rotation frequency to one in line with the literature leads to inferior flow time. In addition, ability of developed algorithm to generate clusters of equivalent solutions in terms of flow time was shown.

In this research, integration of job rotation scheduling and line–cell conversion problems was introduced, considering lack of an integrated look at these two practices in the literature. In addition, a new improved IWO equipped with shake enforcement was introduced.