International Journal of Electrical and Computer Engineering
Institute of Advanced Engineering and Science (IAES)
About: International Journal of Electrical and Computer Engineering is an academic journal. The journal publishes majorly in the area(s): Electric power system & Control theory. It has an ISSN identifier of 2088-8708. It is also open access. Over the lifetime, 4606 publications have been published receiving 25006 citations.
Papers published on a yearly basis
TL;DR: The application and performance comparison of PSO and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design is presented to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.
TL;DR: A new method which applies an artificial bee colony algorithm for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem is presented.
Abstract: Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency. Keywords—Distribution system, Network reconfiguration, Loss reduction, Artificial Bee Colony Algorithm.
TL;DR: The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems.
Abstract: In this paper, the application of neural networks to study the design of short-term temperature forecasting (STTF) Systems for Kermanshah city, west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STTF systems is used. Our study based on MLP was trained and tested using ten years (1996-2006) meteorological data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems. Keywords—Artificial neural networks, Forecasting, Weather, Multi-layer perceptron.
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