In order to model and visualize the performance of lightning and its protection system, several analytical methods such as the rolling sphere method (RSM) have been developed in which protected and unprotected areas a...
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In order to model and visualize the performance of lightning and its protection system, several analytical methods such as the rolling sphere method (RSM) have been developed in which protected and unprotected areas are separated. However, in none of these methods, the scale and severity of the damage can be quantified numerically. In this paper, by using the leader progression model (LPM) in 3D space along with an intelligent algorithm named teaching-learning-basedoptimization (TLBO), a new method is proposed to identify the vulnerable areas with the highest probability of lightning strikes. In using the TLBO algorithm, a new method for determining the critical range of current is defined and the most vulnerable points on the structure are determined, so that, based on the simulation results, the best positions of the lightning rods can be specified respectively. A sample asymmetric structure of height less than 60 meters is analyzed as a case study, and the optimal locations and heights of lightning rods are determined using the rolling sphere method and the proposed method, respectively. The simulation results validate the effectiveness of the proposed method which makes it suitable for other complex structures.
Electricity price forecasting in the electricity market is one of the important purposes for improving the performance of market players and increasing their profits in a competitive electricity market. Since the syst...
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Electricity price forecasting in the electricity market is one of the important purposes for improving the performance of market players and increasing their profits in a competitive electricity market. Since the system load is one of the important factors affecting electricity price changes, a two-factorial model based on fuzzy time series is presented in this paper for electricity price forecasting using the electricity prices of the previous days and the system load. In the proposed method, price and system load time series are fuzzified by fuzzy sets created based on the fuzzy C-means clustering algorithm. After determining proposed model coefficients by the teaching-learning-based optimization algorithm, this model is used for forecasting the next day electricity price. The promising performance of the proposed model is examined using Australia and Singapore electricity markets data.
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