Abstract

The use of distribution automation systems has grown significantly in electricity companies in recent years. The main reason for this is the need to have a smarter network, in order to reduce the time of power interruption. For such applications, communication using radio frequency is preferred because this solution is more reliable than 3G/4G and cheaper than fiber optics. In this context, it is of utmost importance to have a more efficient base transceiver station (BTS) that can cover the communication of a larger area. In this paper, the mathematical concepts of an intelligent antenna array, as well as the logical operation of an intelligent radiation system controller, are presented. Such system receives as inputs the geographic coordinates of network elements and automatically feeds an intelligent Yagi–Uda antenna array with the appropriate parameters, in order to optimize the radiation pattern into the desired directions. The presented model uses a stochastic optimization method to automatically achieve a set of optimal electrical parameters to excite the array and efficiently direct its beams in a fully controlled way. Thus, the results obtained indicate that the proposed intelligent scheme allows the energy optimization of the antenna system, reducing in 61% the number of BTS needed to cover the same area, when compared to traditional collinear antenna systems.

Highlights

  • Automation systems have been widely used by the electrical industry in recent years [1]

  • For the most part of base transceiver station (BTS) solutions used in the electrical sector, the radio frequency (RF) signal is propagated by a collinear array with an omnidirectional propagation [8]

  • To increase the efficiency and reach of Radio Base Stations, it is presented in this paper the theoretical and constructive principles of a RF system that uses a smart Yagi–Uda antenna array with 360-degree coverage electronically controlled. e proposed system receives as input the geographical coordinates regarding the locations of the transmitter and the equipment to be driven, and through an embedded mathematical optimization model, it is able to automatically control the optimal radiation direction of the RF generator power, increasing the range and efficiency of a common BTS. e great advantage of this process is that it provides a high gain concentrated radiation beam in any angular direction of a remote device the BTS needs to communicate with

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Summary

Introduction

Automation systems have been widely used by the electrical industry in recent years [1]. There is a considerable growth in the number of automated equipment, where radio frequency (RF) communication is one of the main means of remote control used by companies to drive remote devices [2, 3] For this application, radio frequency communication is more reliable than 3G/4G, due to its independence of third-party operators, and cheaper than fiber optics, being, the most viable alternative [4, 5]. It is well known that several factors, such as the distance, vegetation, civil construction, and the terrain topology between the outstations and its manager BTS, can affect the signal link quality [9, 10] It can generate disruption in the radio communication, leaving some of these outstations in shadow regions [11]. To increase the efficiency and reach of Radio Base Stations, it is presented in this paper the theoretical and constructive principles of a RF system that uses a smart Yagi–Uda antenna array with 360-degree coverage electronically controlled. e proposed system receives as input the geographical coordinates regarding the locations of the transmitter and the equipment to be driven, and through an embedded mathematical optimization model, it is able to automatically control the optimal radiation direction of the RF generator power, increasing the range and efficiency of a common BTS. e great advantage of this process is that it provides a high gain concentrated radiation beam in any angular direction of a remote device the BTS needs to communicate with

Antenna Array Theoretical Background
Method Using the PSO Optimization
Computational Results for PSO Optimization
Method
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