Wild Goats Optimization Approach for Capacitor Placement Problem

This paper deals with Capacitor Placement (CP) issue. The topic is an optimization problem including two types of variables: capacitor location as an integer variable, capacitor size as a continuous one. To cope with this problem, a new approach entitled Wild Goats Algorithm (WGA) is used. WGA is a new heuristic approach which has been proved recently. In this paper, WGA is successfully implemented to the CP problem with the objective of total loss reduction. Power flow criteria as well as operation constraints are all together accommodated in the process of optimization. Two various scenarios at three load levels are also recognized to cover all possible conditions. The validity of the WGA approach in handling CP problem is assured by testifying it on IEEE 33-bus and 69-bus test systems

Joint optimization of production and maintenance scheduling for unrelated parallel machine using hybrid discrete spider monkey optimization algorithm

This paper considers an unrelated parallel machine scheduling problem with variable maintenance based on machine reliability to minimize the maximum completion time. To obtain the optimal solution of small-scale problems, we firstly establish a mixed integer programming model. To solve the medium and large-scale problems efficiently and effectively, we develop a hybrid discrete spider monkey optimization algorithm (HDSMO), which combines discrete spider monkey optimization (DSMO) with genetic algorithm (GA). A few additional features are embedded in the HDSMO: a three-phase constructive heuristic is proposed to generate better initial solution, and an individual updating method considering the inertia weight is used to balance the exploration and exploitation capabilities. Moreover, a problem-oriented neighborhood search method is designed to improve the search efficiency. Experiments are conducted on a set of randomly generated instances. The performance of the proposed HDSMO algorithm is investigated and compared with that of other existing algorithms. The detailed results show that the proposed HDSMO algorithm can obtain significantly better solutions than the DSMO and GA algorithms

Planning of Unbalanced Radial Distribution Systems with Reactive and Distributed Energy Sources Using Evolutionary Computing Techniques

The distribution system plays a key role in power system as it provides energy to the consumers safely, reliably, and economically. However, due to high R/X ratio, and low operating voltages, most of the losses occur in the distribution system. Moreover, distribution systems are generally unbalanced due to unequal single phase loads at the three phases of the system, and also additional unbalancing is introduced due to non-equilateral conductor spacing. This, causes the voltage, the current, and the power unbalance in the system. Further, the total neutral current of the system increases causing unwanted tripping of the relay. Hence, the service quality and the reliability of the distribution system reduces. Therefore, a suitable phase balancing strategy is required to mitigate the phase unbalancing in the unbalanced distribution systems. Also, apart from reducing the phase unbalancing in the unbalanced distribution systems, a suitable strategy is required to minimize the system power loss. In this regard, it is necessary for the distribution engineers to plan the unbalanced distribution systems in order to reduce the losses, voltage unbalances, and neutral current of the system for safe and reliable operation. Most of the approaches for the planning of the unbalanced distribution systems are based upon metaheuristic algorithms. Moreover, the recent research has focused only on either phase balancing or simultaneous phase balancing and conductor sizing optimization in unbalanced distribution systems using metaheuristic algorithms. However no work has been carried out to study the impact of the simultaneous optimization of the phase balancing, the conductor sizing, the capacitor location and sizing, the DG location and sizing, DSTATCOM location, and rating on system power loss, voltage unbalance, etc. utilizing these algorithms. As the metaheuristic algorithms are random in nature, the convergence is not guaranteed in a single simulation run. Hence, it is necessary to perform a statistical comparison among them in order to understand their relative merits and demerits for multiple simulation runs. In this thesis, the impact of the simultaneous optimization of the phase balancing and the conductor sizing on the planning problems/objective functions of the unbalanced distribution system such as; the power loss, the voltage unbalance, the total neutral current, and the complex power unbalance studied using various metaheuristic algorithms such as the DE, the CSA, the PSO, and the GA. In the first step, these objective functions are optimized separately; then they are aggregated with weights into a multi-objective optimization problem. Further, a performance comparison in terms of the mean value of the objective functions and standard deviation (SD) carried out. The reactive power compensating devices, such as the Capacitor, and the DSTATCOM has been integrated into the planning problem for the power loss minimization, the voltage profile improvement, and the voltage unbalance mitigation of the unbalanced distribution systems. Moreover, a three phase unbalanced modelling of the DSTATCOM has been developed. In this thesis, the effect of the simultaneous optimization of the phase balancing, the conductor sizing, the capacitor sizing, and the simultaneous optimization of the phase balancing, the conductor sizing, and the DSTATCOM sizing on the planning problem investigated. Both, single and multi-objective optimization approach are used in order to solve this problem. Also, statistical performance among the metaheuristic algorithms such as; the DE, the CSA, the PSO, and the GA in terms of the mean value of the objective function and SD carried out. Further, the renewable sources such as the DG and a combined DG and DSTATCOM has been incorporated into the unbalanced system in order to study their impact on various planning problems

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications