Oppositional Differential Search Algorithm for the Optimal Tuning of Both Single Input and Dual Input Power System Stabilizer

Low frequency oscillation has been a major threat in large interconnected power system. These low frequency oscillations curtain the power transfer capability of the line. Power system stabilizer (PSS) helps in diminishing these low frequency oscillations by providing auxiliary control signal to the generator excitation input, thereby restoring stability of the system. In this chapter, the authors have incorporated the concept of oppositional based learning (OBL) along with differential search algorithm (DSA) to solve PSS problem. The proposed technique has been implemented on both single input and dual input PSS, and comparative study has been done to show the supremacy of the proposed techniques. The convergence characteristics as well authenticate the sovereignty of the considered algorithms.

Multi-Fuel Power Dispatch Considering Prohibited Operating Zones and Tie-Line Flow Limits Using Ant Lion Optimizer

The electrical power generation from fossil fuel releases several contaminants into the air, and these become excrescent if the generating unit is fed by multiple fuel sources (MFS). The ever more stringent environmental regulations have forced the utilities to produce electricity at the cheapest price and the minimum level of pollutant emissions. The restriction in generator operations increases the complexity in plant operations. The cost effective and environmental responsive operations in MFS environment can be recognized as a multi-objective constrained optimization problem. The ant lion optimizer (ALO) has been chosen as an optimization tool for solving the MFS dispatch problems. The fuzzy decision-making mechanism is integrated in the search process of ALO to fetch the best compromise solution (BCS). The intended algorithm is implemented on the standard test systems considering the prevailing operational constraints such as valve-point loadings, CO2 emission, prohibited operating zones and tie-line flow limits.

Prospects for Energy Supply of the Arctic Zone Objects of Russia Using Frost-Resistant Solar Modules

The scientific work is devoted to the prospect of using frost-resistant solar modules with extended service life of various designs for energy supply of infrastructure facilities of the Arctic zone of Russia. The general characteristic of the region under consideration is given, and its energy specifics, directions of energy development based on renewable energy sources are considered. In the work, frost-resistant planar photovoltaic modules and solar roofing panels with an extended service life for power supply of objects are proposed. For simultaneous heat and electrical generation, frost-resistant planar photovoltaic thermal roofing panels and concentrator solar installation with high-voltage matrix solar modules with a voltage of 1000 V and an electrical efficiency of up to 28% are proposed. The considered solar modules have an extended rated power period due to the use of the technology of sealing solar cells with a two-component polysiloxane compound and are able to work effectively at large negative ambient temperatures and large ranges of its fluctuations.

Prediction of Menstrual Cycle Phase by Wearable Heart Rate Sensor

The prediction of the menstrual cycle phase and fertility window by easily measurable bio-signals is an unmet need and such technological development will greatly contribute to women's QoL. Although many studies have reported differences in autonomic indices of heart rate variability (HRV) between follicular and luteal phases, they have not yet reached the level that can predict the menstrual cycle phases. The recent development of wearable sensors-enabled heart rate monitoring during daily life. The long-term heart rate data obtained by them carry plenty of information, and the information that can be extracted by conventional HRV analysis is only a limited part of it. This chapter introduces comprehensive analyses of long-term heart rate data that may be useful for revealing their associations with the menstrual cycle phase.

Smart Connected Digital Products and IoT Platform With the Digital Twin

A digital illustration of a novel prevalence of a physical product helps one to gain larger insight into that product's state performance and behavior digital twin, which is an unequivocal advanced copy of an item, method, or control. This living model creates a thread between the physical and digital worlds. A model of a physical object—a 'twin'—enables you to observe its standing, diagnose problems, and take a look at solutions remotely. It's a dynamic virtual illustration of a tool that is unendingly fed with knowledge from embedded sensors and packages. This provides associate degree correct period of time standing of the physical device. Digital twins drive innovation and performance and offer development technicians prognostic analytics that give firms the flexibility to boost client expertise.

Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization

The increasing complexity of engineering systems has spurred the development of highly efficient optimization techniques. This chapter focuses on two novel optimization methodologies: extreme value stochastic engines (random number generators) and the coupled map lattice (CML). This chapter proposes the incorporation of extreme value distributions into stochastic engines of conventional metaheuristics and the implementation of CMLs to improve the overall optimization. The central idea is to propose approaches for dealing with highly complex, large-scale multi-objective (MO) problems. In this work the differential evolution (DE) approach was employed (incorporated with the extreme value stochastic engine) while the CML was employed independently (as an analogue to evolutionary algorithms). The techniques were then applied to optimize a real-world MO Gas Turbine-Absorption Chiller system. Comparative analyses among the conventional DE approach (Gauss-DE), extreme value DE strategies, and the CML were carried out.

Improving the Optimality Verification and the Parallel Processing of the General Knapsack Linear Integer Problem

The chapter presents a new approach to improve the verification process of optimality for the general knapsack linear integer problem. The general knapsack linear integer problem is very difficult to solve. A solution for the general knapsack linear integer problem can be accurately estimated, but it can still be very difficult to verify optimality using the brach and bound related methods. In this chapter, a new objective function is generated that is also used as a more binding equality constraint. This generated equality constraint can be shown to significantly reduce the search region for the branch and bound-related algorithms. The verification process for optimality proposed in this chapter is easier than most of the available branch and bound-related approaches. In addition, the proposed approach is massively parallelizable allowing the use of the much needed independent parallel processing.

Visual Analytics to Build a Machine Learning Model

One of the most interesting and promising areas of development of machine learning is the active involvement of a human in the process of building a model. However, there are problems with the effective integration of humans into a workflow. It is necessary to develop techniques and information technologies that would allow the effective use of human intellectual capabilities, thereby expanding the machine learning tools. This work considers the use of visual analytics with the goal of building a machine learning model by a human and the technique of transferring this model to the machine level. This made it possible to expand the capabilities of machine learning through the active and productive use of human intellectual abilities.

New Direction to the Scheduling Problem

This chapter presents a new direction to the scheduling problem by exploring the Moore-Hodgson algorithm. This algorithm is used within the context of integer programming to come up with complementarity conditions, more biding constraints, and a strong lower bound for the scheduling problem. With Moore-Hodgson Algorithm, the alternate optimal solutions cannot be easily generated from one optimal solution; however, with integer formulation, this is not a problem. Unfortunately, integer formulations are sometimes very difficult to handle as the number jobs increases. Therefore, the integer formulation presented in this chapter uses infeasibility to verify optimality with branch and bound related algorithms. Thus, the lower bound was obtained using pre-processing and shown to be highly accurate and on its own can be used in those situations where quick scheduling decisions are required.

Optimal Sizing of Hybrid Wind and Solar Renewable Energy System

If properly designed and utilized, earth has rich potential of clean energy in satisfying the energy demand of the world. In this chapter, nature-inspired methodology was employed to optimize hybrids of renewable energy system in the case of Jeldu district of Ethiopia. The main goal of the researchers here is to minimize the total annual cost of the system, which can be designed by using appropriate numbers of components based on the pre-designed constraints to satisfy the load demand. MATLAB code was designed for the proposed methodology, and the results were discussed. It was seen from the result that the proposed approach has solved the optimum sizing of defined problem with high convergence. The results show that energy demand of the village can be optimally satisfied by the use of wind and solar hybrid system. Moreover, the application of this chapter is important for countries like Ethiopia to increase access to electricity.