11,235 research outputs found
GA tuning of pitch controller for small scale MAVs
The paper presents the application of intelligent tuning methods for the control of a prototype MAV in order to address problems associated with bandwidth limited actuators and gust alleviation. Specifically, as a proof of concept, the investigation is focused on the pitch control of a MAV. The work is supported by experimental results from wind tunnel testing that shows the merits of the use of Genetic Algorithm (GA) tuning techniques compared to classical, empirical tuning methodologies. To provide a measure of relative merit, the controller responses are evaluated using the ITAE performance index. In this way, the proposed method is shown to induce far superior dynamic performance compared to traditional approaches
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future
Smart Microgrids: Overview and Outlook
The idea of changing our energy system from a hierarchical design into a set
of nearly independent microgrids becomes feasible with the availability of
small renewable energy generators. The smart microgrid concept comes with
several challenges in research and engineering targeting load balancing,
pricing, consumer integration and home automation. In this paper we first
provide an overview on these challenges and present approaches that target the
problems identified. While there exist promising algorithms for the particular
field, we see a missing integration which specifically targets smart
microgrids. Therefore, we propose an architecture that integrates the presented
approaches and defines interfaces between the identified components such as
generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid
Worksho
Initialization of a Multi-objective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants
pp. 185-204The design of Renewable Energy Power Plants (REPPs) is crucial not only for the
investments' performance and attractiveness measures, but also for the maximization of
resource (source) usage (e.g. sun, water, and wind) and the minimization of raw
materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te)
consumption. Hence, several appropriate and satisfactory Multi-objective Problems
(MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only
be managed by very well organized knowledge acquisition on all REPPs' design
equations and models. The proposed MOPs need to be solved with one or more multiobjective algorithm, such as Multi-objective Evolutionary Algorithms (MOEAs). In this
respect, the first aim of this research study is to start gathering knowledge on the REPPs'
MOPs. The second aim of this study is to gather detailed information about all MOEAs
and available free software tools for their development. The main contribution of this
research is the initialization of a proposed multi-objective evolutionary algorithm
knowledge acquisition system for renewable energy power plants (MOEAs-KAS-FREPPs) (research and development loopwise process: develop, train, validate, improve,
test, improve, operate, and improve). As a simple representative example of this
knowledge acquisition system research with two selective and elective proposed
standard objectives (as test objectives) and eight selective and elective proposed
standard constraints (as test constraints) are generated and applied as a standardized
MOP for a virtual small hydropower plant design and investment. The maximization of
energy generation (MWh) and the minimization of initial investment cost (million €)
are achieved by the Multi-objective Genetic Algorithm (MOGA), the Niched Sharing
Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the
NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all
proposed standardized MOEAs on two desktop computer configurations (Windows 10
Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet
connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB
RAM with internet connection). The algorithm run-times (computation time) of the
current applications vary between 20.64 and 59.98 seconds.S
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Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review
YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000
A simplified analytical approach for optimal planning of distributed generation in electrical distribution networks
DG-integrated distribution system planning is an imperative issue since the installing of distributed generations (DGs) has many effects on the network operation characteristics, which might cause significant impacts on the system performance. One of the most important characteristics that mostly varies because of the installation of DG units is the power losses. The parameters affecting the value of the power losses are number, location, capacity, and power factor of the DG units. In this paper, a new analytical approach is proposed for optimally installing DGs to minimize power loss in distribution networks. Different parameters of DG are considered and evaluated in order to achieve a high loss reduction in the electrical distribution networks. The algorithm of the proposed approach has been implemented using MATLAB software and has been tested and investigated on 12-bus, 33-bus, and 69-bus IEEE distribution test systems. The results show that the proposed approach can provide an accurate solution via simple algorithm without using exhaustive process of power flow computations
An Adaptive Overcurrent Coordination Scheme to Improve Relay Sensitivity and Overcome Drawbacks due to Distributed Generation in Smart Grids
Distributed Generation (DG) brought new challenges for protection engineers since standard relay settings of traditional system may no longer function properly under increasing presence of DG. The extreme case is coordination loss between primary and backup relays. The directional overcurrent relay (DOCR) which is the most implemented protective device in the electrical network also suffers performance degradation in presence of DG.
Therefore, this paper proposes the mitigation of DG impact on DOCR coordination employing adaptive protection scheme (APS) using differential evolution algorithm (DE) while improving overall sensitivity of relays .
The impacts of DG prior and after the application of APS are presented based on interconnected 6 bus and IEEE 14 bus system. As a consequence, general sensitivity improvement and mitigation scheme is proposed
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