Structure optimization of electrical power network using ant colony approach

Today's important industrial companies seek maximum benefit with minimum investment. In order to achieve this goal, design engineers depend on cost optimisation techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimisation method to solve the problem of power systems design optimisation. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterised by their cost, capacity and reliability. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of electrical power components with different parameters are selected from a list of products available in the market. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and HS algorithm. An illustrative example is presented.

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In this paper, an algorithm based ant colony approach (ACA) is designed to find the optimal configuration for redundancy apportionment problem (RAP) of series-parallel multi-state system (MSS). In order to achieve maximum system reliability under cost and performance constraints, the algorithm takes advantage of ACA’s combinatorial optimization, uses heuristic information about reliability to arrange components from available choice. Different from traditional nominal performance and reliability system, universal generating function (UGF) is introduced and used to estimate the multi-state system reliability. Finally the experiment has shown that the presented algorithm can provide optimal and near optimal solutions, and have efficient and convenient calculation performance.

Many biological processes are governed by large assemblies of protein molecules. However, it is often very difficult to determine the three-dimensional structures of these assemblies using experimental biophysical techniques. Hence there is a need to develop computational approaches to fill this gap. This article presents an ant colony optimization approach to predict the structure of large multi-component protein complexes. Starting from pair-wise docking predictions, a multi-graph consisting of vertices representing the component proteins and edges representing candidate interactions is constructed. This allows the assembly problem to be expressed in terms of searching for a minimum weight spanning tree. However, because the problem remains highly combinatorial, the search space cannot be enumerated exhaustively and therefore heuristic optimisation techniques must be used. The utility of the ant colony based approach is demonstrated by re-assembling known protein complexes from the Protein Data Bank. The algorithm is able to identify near-native solutions for five of the six cases tested. This demonstrates that the ant colony approach provides a useful way to deal with the highly combinatorial multi-component protein assembly problem.

Power system is one of the most complex systems ever designed. Determining its reliability is therefore a demanding process, which is mostly dealt with in a way that partial problems are stated and solved. A reliability method of loss of load expectation is selected and improved and the model of conventional power system is considered as a standpoint. Reliability of the initial conventional power system is evaluated. Then, the conventional power system is changed, where one conventional power plant is abandoned and new wind power plants stand to its place. The reliability of the initial and the changed power system is calculated and compared. Discussion shows the problems of decreased reliability of the power system with increasing the percentage of wind power plants within the power system replacing conventional plants, if not enough reserve is added to the power system at the same time. The other related problems are discussed in addition, to show that the problem is very complex and one cannot only consider a limited number of influencing parameters when deciding on power system configurations.

Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures

The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to the total electric capacity. With growing importance of intermittent wind and solar generation taking part into power systems worldwide, there is need for greater chronological resolution to estimate the flexibility of the power system to offer firm capacity. In this way, a linear optimization model operating hourly is developed to calculate the minimum power system cost, while stablishing the capacity allocation to meet the projected load throughout one-year simulation, as an estimation of how the hourly economic dispatch impacts the scheduling of generators belonging to a power system with this portfolio composition. A central focus is how to operate the available hydro capacity to back up intermittent renewables, evaluating the physical hydro operating constraints, monthly energy balance and maximum power availability. A case study was simulated based on the Brazil’s power system configuration, showing that existing hydro capacity provide hourly flexibility to back-up intermittent renewables, potentially saving 1.2 Billion R$, about 3.6% of total system cost referred to 2019. It is worthwhile to realize that the developed methodology can be employed to other power systems with similar capacity portfolio structure for the purpose of calculating its optimum allocation for a specified region and target year.

Pulsed power systems for directed energy weapon platforms are becoming increasingly important in modern combat situations. A general thermodynamic analytical investigation was developed to assess the impact of technological improvements on mission effectiveness and weapon power generation in an aircraft (a/c) based pulsed power system. The power system, for which the analysis is being conducted, essentially consists of six major components, a prime power source, electrical power generator, power conditioner, pulsed power source and pulsed power processor and the thermal management system (TMS) with a total estimated payload restriction of 4600 kgs., based on a USAF cargo aircraft. The a/c based power systems analysis corresponds to a notional aircraft mission, with the turbo- shaft engine operating at a fixed altitude. It has been assumed that heat from various system components is collected by the same coolant loop, and then rejected to ambient air in a ram air heat exchanger (RAHX). The analysis was based on a Multi-MW class pulsed power source output and a notional mission profile with an engagement period of 60 minutes during which several duty cycle scenarios were considered. The power system components were sized to accommodate the power requirement represented by the respective duty cycle of operation. Several power system architectures were evaluated with a baseline power system model that incorporated current off-the-shelf (OTS) technologies for each component. The baseline power systems analysis was conducted to size a power system for steady state operation at peak load during an individual engagement. A stand-alone turbo shaft engine was used as the primary power source because of its high power density but the engine performance is very sensitive to increasing altitude where the output power diminishes rapidly. As a result of this and the necessity to accommodate load sharing or load following during engagement, the investigations were extended to the hybrid power system architecture with turbo alternator-battery and turbo alternator-flywheel combinations.

This paper describes two novel methods for distributed simulation of nonlinear power system dynamics, which are named two-level integration and interaction-level integration. An underlying assumption is that the simulation of dynamic response is distributed spatially, i.e. each power system component and/or each group of components perform numerical integration of their own states while occasionally communicating relevant information with their neighbors. The novelty of the proposed methods is in exploiting the structure of dynamic equations of typical power system components to shorten the total simulation time in this distributed setting. The proposed methods consist of a number of state transformations on the dynamical power system model which bring this model into a form favored for distributed numerical integration. These transformations, proposed first in [1] and [2] for control design, separate internal component dynamics from dynamics of its interactions with neighboring components. It is further shown on a simple example that it is sufficient to exchange the information between components at a rate determined by the dynamics of the interaction variables to successfully perform numerical integration of the entire power system model with a significant improvement in simulation time. Since the proposed methods are based on the state transformation of the power system model, this approach could be used together with other numerical and machine-level programming optimization techniques to further enhance performance of the power system dynamics simulator.

Testing of a small-scale stand-alone power system based on solar energy and hydrogen

The U.S. Army TACOM-TARDEC developed and validated a high-fidelity six-degree-of-freedom model to use in a trade study for the development of a prototype autonomous vehicle. The model captures realistic dynamics of the six-wheeled, skid-steered vehicle along with the electrical, thermal, and mechanical response of a detailed series hybrid-electric power system with in-hub drive motors, lithium-ion battery, and generator linked to a diesel engine. These components were modeled and integrated via extensive power and energy component libraries developed for use with a high-fidelity software tool for dynamics modeling. Further, the vehicle model’s entire complement of components was integrated in a flexible configuration that allowed them to be readily adjusted or swapped out so the user could use the model to ascertain the relative effects of modifying the vehicle’s structural or power system components on specific vehicle evaluation criteria. Such criteria include the vehicle’s performance with high-speed stability, skid steering stability, body pitch/roll/dive/squat characteristics, braking capability, road/soft-soil traversal, and steering maneuverability.The model captures both the on- and off-road mobility for the vehicle via use of an extensive library of various terrains including hard surface, sand, sandy loam, clay soil, and snow. Further, detailed waypointbased path navigation routines automate the vehicle’s traversal over a number of user-selectable courses including some established military courses such as Churchville-B, Perryman 1, 3, and A, and Munson with user-defined vehicle velocities. The model functions as an executable file run independent of any proprietary or close-source software; the user utilizes a simplified interface to vary any of the variables associated with the vehicle’s geometry, power system, course and speed to navigate, and terrain type applied to the course. The graphical view for the vehicle traversing the selected terrain is shown with an open source 3D graphics tool. The model was validated by applying the specifications in the model for the prototype vehicle of the first-generation of autonomous six-wheeled skid-steered vehicle, simulating the model in maneuvers identical to those the prototype vehicle performed, and comparing the simulated and actual results; the data matched and the model was successfully validated.The vehicle model was designed primarily for the trade study for the design of a specific vehicle, but was created with sufficient flexibility and capability for modeling future vehicles as well. The interchangeability of the vehicle models’ components and environments allow a user to modify or replace the vehicle’s power system components, chassis masses, tires, transmission, duty cycles, courses to traverse, and many other aspects of the vehicle. Thus the user can essentially model any vehicle with similar types of components or structures and use that model to determine the impact of those elements upon many vehicle design considerations such as mass requirements, volume constraints, power system requirements, wheels design, suspension characteristics, and controls. Several new vehicle models are already being developed using this model’s flexibility and capability.

The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.

Scheduling is regarded as one of the vital decision-making processes used frequently in many real-time cases. It manages everything from resource allocation to the task completion, with the goal to optimize the desired objectives. Subject to the problem, the resources, tasks, and goals can differ. The aim is to design a corporative multiagent system for optimal scheduling. Many of the scheduling available algorithms calculate optimality based on different perspectives. The proposal is to create the dataset using multiple algorithms with different performance metrics to find an optimal one. This data can be imported into machine learning tools for training and predicting, based on the selected performance metrics. The algorithm considered in the empirical analysis includes first come first serve, Round robin, and Ant colony approach. The major finding shows that scheduling using Ant colony is an optimal algorithm, which is based on speed and velocity. The future extension would be to check the correctness of optimality using machine learning tools.

In this letter, we address the problem of minimizing the energy consumption of disseminating a library of files to a set of Internet-of-Things (IoT) devices using an unmanned aerial vehicle (UAV). A framework is provided, in which a subset of devices are selected to receive data from a UAV and then forward the required data to other devices. Furthermore, optimal energy-efficient path selection is considered in order to realize efficient data dissemination. Specifically, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to disseminate the required files to the former. An ant colony optimization (ACO) algorithm is developed to solve the optimization problem. Simulation results show that the proposed framework is more energy-efficient compared to a baseline approach, where the UAV hovers above each device to deliver the data. Results also illustrate that the proposed ACO algorithm provides performance close to the optimal solution, which is obtained through exhaustive search.

As a reaction to the volatile market demands with regards to the number and variants of products offered, ever more complex procedures for manufacturing control are being developed. Most recently, self-organising procedures, which often mimic the behaviour of natural systems, have arisen. The method of ant colony optimisation (ACO), which was inspired by ants, can provide the necessary fundamentals in order to realise self-organising manufacturing control. In this context, the ifab-Institute has developed the AntControl tool for self-organising manufacturing control based on ACO. In order to investigate the potential of ACO, several concepts have been developed and integrated into the existing OSim simulation tool to create the new OSim-Ant tool. An exemplary simulation study within a manufacturing system has been carried out to evaluate the behaviour of AntControl. This paper presents this tool as well as the results of the simulation study.