12,206 research outputs found
Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy
Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind
power generator.ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΡΠ»ΡΡΠΈΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π½Π°Π±ΠΎΡΠ° Π΄Π²ΠΎΠΈΡΠ½ΡΡ
ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠ² ΠΈ ΠΈΡ
ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΈ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΌΠ°ΡΠΈΠ½Π° ΠΎΠΏΠΎΡΠ½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠΌ Π²ΡΡ
ΠΎΠ΄Π½ΡΡ
ΠΊΠΎΠ΄ΠΎΠ² ΠΈΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΡΠΈΠ±ΠΎΠΊ(ECOC-SVM) Ρ ΡΠ΅Π»ΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ ΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°ΡΡ ΡΠ°ΠΊΠΈΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ, ΠΊΠ°ΠΊ Π³Π°ΡΠΌΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ ΠΈ ΡΠΊΠ°ΡΠΎΠΊ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π²ΠΊΠ»ΡΡΠ°Ρ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ Π²Π΅ΡΡΠΎΠ²ΡΡ
ΡΠ»Π΅ΠΊΡΡΠΎΡΡΠ°Π½ΡΠΈΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. Π‘Π½Π°ΡΠ°Π»Π° Π²ΡΠΏΠΎΠ»Π½ΡΠ΅ΡΡΡ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡΠΎΠΊΠ° Π½Π΅ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΠΎΠΉ Π½Π°Π³ΡΡΠ·ΠΊΠΈ ΡΡΠ΅Ρ
ΡΠ°Π· Π΄Π»Ρ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ°Π·Π½ΠΎΡΡΠ½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ, ΡΡΠΎΠ²Π½Π΅ΠΉ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΈ ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ. ΠΠΎΡΠ»Π΅ ΡΡΠΎΠ³ΠΎ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ΅ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΠ΅ΡΡΡ Ρ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΡΡ ECOC-SVM Π΄Π»Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ°. ΠΠ°ΠΊΠΎΠ½Π΅Ρ, ECOC-SVM ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΡΠΈΠΏ Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Π΅Ρ ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ 99,2% ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Ρ
ΠΎΡΠΎΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΈ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΊΠ°ΠΆΠ΄ΠΎΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ ΠΈΠΌΠ΅Π΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠ΅ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΈΡΡΠΎ ΡΠΈΠ½ΡΡΠΎΠΈΠ΄Π°Π»ΡΠ½ΠΎΠΉ ΡΠΎΡΠΌΡ Π²ΠΎΠ»Π½Ρ, ΡΡΠΎ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΈΠΏΠ° Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ, Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ Π²Π΅ΡΡΠΎΠ²ΡΠΌ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΎΠΌ
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
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Artificial Immune Systems - Models, algorithms and applications
Copyright Β© 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Optimized Solar Photovoltaic Generation in a Real Local Distribution Network
Remarkable penetration of renewable energy in electric networks, despite its
valuable opportunities, such as power loss reduction and loadability
improvements, has raised concerns for system operators. Such huge penetration
can lead to a violation of the grid requirements, such as voltage and current
limits and reverse power flow. Optimal placement and sizing of Distributed
Generation (DG) are one of the best ways to strengthen the efficiency of the
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network based on obtained load profiles, GIS information, solar insolation,
feeder and voltage settings, and define the optimization problem of solar PVDG
installation to determine the optimal siting and sizing for different
penetration levels with different objective functions. The objective functions
include voltage profile improvement and energy loss minimization and the
considered constraints include the physical distribution network constraints
(AC power flow), the PV capacity constraint, and the voltage and reverse power
flow constraints.Comment: To be published (Accepted) in: Proceedings of the IEEE PES Innovative
Smart Grid Technologies Conference (ISGT), Washington D.C., USA, 201
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
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