Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Modelling and control of hybrid electric vehicles (a comprehensive review)

The gradual decline in global oil reserves and presence of ever so stringent emissions rules around the world, have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guarantying improved fuel economy and reduced emissions.The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilized. The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle.To this effect, a number of power management strategies have been proposed in literature. This paper presents a comprehensive review of these literatures, focusing primarily on contributions in the aspect of parallel hybrid electric vehicle modelling and control. As part of this treatise, exploitable research gaps are also identified. This paper prides itself as a comprehensive reference for researchers in the field of hybrid electric vehicle development, control and optimization

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Optimal Control of Power Quality in Microgrids Using Particle Swarm Optimisation

Driven by environmental protection, economic factors, conservation of energy resources, and technical challenges, the microgrid has emerged as an innovative small-scale power generation network. Microgrids consist of a cluster of Distributed Generation units that encompass a portion of an electric power distribution system and may rely on different energy sources. Functionally, the microgrid is required to provide adequate levels and quality of power to meet load demands. The issue of power quality is significant as it directly affects the characteristics of the microgrid’s operation. This problem can be defined as an occurrence of short to long periods of inadequate or unstable power outputs by the microgrid. In a stand-alone operation mode, the system voltage and frequency must be established by the microgrid, otherwise the system will collapse due to the variety in the microgrid component characteristics. The harmonic distortion of the output power waveforms is also a serious problem that often occurs because of the high speed operation of the converter switches. The long transient period is a critical issue that is usually caused by changing the operation mode or the load demand. Power sharing among the Distributed Generation units is also an important matter for sharing the load appropriately, particularly given that some renewable energy resources are not available continuously. In a utility connected microgrid, the reliable power quality mainly depends on the regulation of both active and reactive power, because the microgrid’s behaviour is mostly dominated by the bulk power system. Therefore, an optimal power control strategy is proposed in this thesis to improve the quality of the power supply in a microgrid scenario. This controller comprises an inner current control loop and an outer power control loop based on a synchronous reference frame and conventional PI regulators. The power control loop can operate in two modes: voltage-frequency power control mode and active-reactive power control mode. Particle Swarm Optimisation is an intelligent searching algorithm that is applied here for real-time self-tuning of the power control parameters. The voltage-frequency power controller is proposed for an inverter-based Distributed Generation unit in an autonomous operation mode. The results show satisfactory system voltage and frequency, high dynamic response, and an acceptable harmonic distortion level. The active-reactive power controller is adopted for an inverter-based Distributed Generation unit in a utility operation mode. This controller provides excellent regulation of the active and reactive power, in particular when load power has to be shared equally between the microgrid and utility. The voltage-frequency and active-reactive power control modes are used for a microgrid configured from two DG units in an autonomous operation mode. The proposed control strategy maintains the system voltage and frequency within acceptable limits, and injects sustained output power from one DG unit during a load change. The reliability of the system’s operation is investigated through developing a small-signal dynamic model for the microgrid. The results prove that the system was stable for the given operating point and under the proposed power controller. Consequently, this research reveals that the microgrid can successfully operate as a controllable power generation unit to support the utility, thus reducing the dependency on the bulk power system and increasing the market penetration of the micro-sources

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular multi-objective optimization approach is investigated for the advanced optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (MICINN)–Agencia Estatal de Investigación (AEI), and by the European Regional Development Funds (ERDF), a way of making Europe, under Grant PGC2018-098946-B-I00 funded by MCIN/AEI/10.13039/501100011033/.Peer ReviewedPostprint (published version

Intelligent transient calibration of a dual-loop EGR diesel engine using chaos-enhanced accelerated particle swarm optimization algorithm

This article proposes an intelligent transient calibration method for the air-path controller of a light-duty diesel engine. This method is developed based on the chaos-enhanced accelerated particle swarm optimization algorithm. The target is to reduce the engine’s fuel consumption during transient scenarios by optimizing the controller parameters. The advanced dual-loop exhaust gas recirculation system is first introduced. Then, it formulates the transient calibration process as a multiple-objective optimization problem with constraints. Different from steady state calibration, the proposed method designs a new cost-function to evaluate the controller’s transient performance. The intelligent transient calibration module is programmed in MATLAB code. Interface between the calibration module and a physical engine plant is established via ETAS INCA. The optimization result of the proposal method is discussed by comparing it with the result of existing calibration methods. The engine performance with the calibrated controller is evaluated based on engine tests. </jats:p

Voltage control strategies for loss minimzation in autonomous microgrids

This dissertation investigates the novel idea of flexible-voltage autonomous microgrids (MG), employing several interconnectable dc buses operating in a minimum-voltage mode. In comparison with the traditional fixed-voltage MGs, the proposed MGs reduce losses to gain significant enhancement in efficiency. It is widely believed that energy systems of the future will heavily depend on MGs rich in power electronics converters (PECs). This dissertation is focused on MGs with a high degree of self-sufficiency, without precluding sporadic links with the power grid. Potential applications of those MGs include: (a) distributed generation power systems, (b) ships, land vehicles, aircraft, and spacecraft, (c) users in need of power supply impervious to vulnerabilities of the grid, and (d) localities lacking an access to a grid.Modern pulse-width modulated PECs allow rapid and wide-range changes of voltages and currents. High switching frequencies result in high power quality and fast dynamic response, but each switching event causes energy loss related to the magnitudes of input voltage and output current. In the existing MGs, the bus voltages are maintained at a fixed level. However, many heavy loads, such as electric drives, operate most of the time with a reduced voltage, which is adjusted by decreasing the voltage gain of the feeding converter. This makes the voltage pulses high and narrow. If instead the pulses were made wide and low, then with the current unchanged the conduction losses would remain unchanged, but the switching losses would greatly decrease. This observation leads to the main idea of the dissertation, namely MGs whose dc-bus voltages are allowed to fluctuate and which are maintained at the lowest possible level. Loss minimization, apart from energy savings, may be critical for autonomous MGs with a tight balance of power.In this dissertation, two methods are proposed for calculating the minimum (optimum) required dc voltage level. In the first method, a central control unit allocates the minimum required dc voltages to individual buses by employing the information obtained from control systems of the adjustable voltage loads. For example, most of the variable-speed ac motors employ the so-called constant volts per hertz strategy, in which the relation between frequency and voltage is clearly specified. In the more sophisticated high-performance drives, the instantaneous values of the desired speed, torque, and current are available, allowing the required voltage estimation from the equation of power balance.In the second method, the problem of determining the optimal dc voltage and power settings is formulated as an optimization problem with the objective function of minimizing the converter losses. Genetic algorithm is utilized in solving the optimization problem. Due to limited available power from renewables, reducing the converter losses will enhance the survivability of the microgrid and ease the cooling requirements, resulting in a more compact system. A model of a 20-bus microgrid with the dc distribution network is employed to verify the effectiveness of the proposed methods