5,038 research outputs found
Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios
This paper presents and evaluates the performance of an optimal scheduling
algorithm that selects the on/off combinations and timing of a finite set of
dynamic electric loads on the basis of short term predictions of the power
delivery from a photovoltaic source. In the algorithm for optimal scheduling,
each load is modeled with a dynamic power profile that may be different for on
and off switching. Optimal scheduling is achieved by the evaluation of a
user-specified criterion function with possible power constraints. The
scheduling algorithm exploits the use of a moving finite time horizon and the
resulting finite number of scheduling combinations to achieve real-time
computation of the optimal timing and switching of loads. The moving time
horizon in the proposed optimal scheduling algorithm provides an opportunity to
use short term (time moving) predictions of solar power based on advection of
clouds detected in sky images. Advection, persistence, and perfect forecast
scenarios are used as input to the load scheduling algorithm to elucidate the
effect of forecast errors on mis-scheduling. The advection forecast creates
less events where the load demand is greater than the available solar energy,
as compared to persistence. Increasing the decision horizon leads to increasing
error and decreased efficiency of the system, measured as the amount of power
consumed by the aggregate loads normalized by total solar power. For a
standalone system with a real forecast, energy reserves are necessary to
provide the excess energy required by mis-scheduled loads. A method for battery
sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201
Development of Hybrid Photovoltaic-Wind System for LED Street Lighting
This paper presents the development of hybrid Photovoltaic (PV)-Wind system
connected to light emitting diode (LED) street lighting as the load. The aim of this research is to
analyze the energy performance from the combination of PV system and wind system which is
hybrid system to the LED lamp as the load. In this project, the PV panel and wind turbine used to
generate the energy and the output of the energy control by two equipments which are solar charger
controller and hybrid charger controller. The output of both PV and wind connected with the battery
as storage energy for this system. In this research, the PV system produces the best energy
compared to the wind system and it is reliable to charge the battery of the system and supply to the
loa
A Review on Expert System Applications in Power Plants
The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator’s actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system.DOI:http://dx.doi.org/10.11591/ijece.v4i1.502
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
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