Significant growth in broadband wireless services, as well as ever-increasing demand on the spectrum caused by the Internet of Things (IoT) have overstretched limited available spectrum space for wireless services. Heterogeneous wireless networks (HetNets)—wherein multiple wireless technologies (e.g., Wi-Fi, Bluetooth, Zigbee, LTE, and GSM) coexist and share spectrum—are a promising solution for enhancing spectrum sharing. An essential element in developing coexistence protocols is correctly identifying wireless technologies anticipated to share spectrum and to shift users between available wireless technologies in an effort to optimize spectrum usage and minimize interference. For the coexistence research reported in this paper, we analyzed the performance of our developed novel algorithm based on dynamic mode decomposition (DMD) mathematical modeling to identify and differentiate among various wireless technologies. More specifically, our technique identified GSM and LTE signals in the cellular domain, IEEE802.11n, ac, and ax in the Wi-Fi domain, as well as Bluetooth and Zigbee. The proposed DMD-based technique identifies the time domain signature of a signal by capturing embedded periodic features transmitted within the signal. Performance and accuracy were tested and validated using an experimental dataset collected for various time series, and raw-power measurements of the targeted technologies. Results showed that the developed DMD-based algorithm can differentiate and classify individual and coexisting wireless signals with high accuracy —greater than 90% for most cases. Furthermore, only a short time— less than one second—is required for identifying a signal and enabling implementation in real-time practical networks. The advantage of the developed technique over comparable techniques is lower complexity (i.e., shorter processing and training time, no channel estimation, no time/frequency synchronization, and no need for long observation-time intervals).

Advancements in wireless technologies and ever-increasing growth in telecommunication systems mean that devices operating the license-free Industrial, Scientific, and Medical (ISM) band have become increasingly inexpensive and widely available. Employing Bluetooth and Zigbee-based wireless devices in the realm of the Internet of Things (IoT), smart cities, and medical applications has simultaneously intensified during recent years. Because these devices share the 2.4 GHz ISM band, achieving successful coexistence to mitigate interference and enhance spectrum sharing have become a vital concern. In this paper, we develop novel techniques based on dynamic mode decomposition (DMD) modeling to identify Bluetooth and Zigbee technologies coexisting in an experimental heterogeneous network. The proposed technique is based on identifying the time domain signature of time series raw power measurements by capturing embedded periodicity features transmitted within the signal. The advantage of the proposed technique over comparable techniques is that it does not require channel estimation, time/frequency synchronization, or long observation intervals. Our work evaluated the performance of the proposed methods to detect and differentiate between targeted signals in terms of accuracy and processing time required to identify a signal. In addition, the performance was compared with deep learning models for validation and evaluation.

Microgrids are one of the main drivers in achieving sustainable energy management in the context of smart cities and smart regions. In this way, multiple energy sources are employed and overall system performance is given by adequate information handling in terms of energy consumption requirements as well as user behavior profiles. This paper introduces a framework for wireless mesh communication, monitoring, and distributed energy management for domestic microgrids. A communication scheme based on a combination of sensors which describe energy consumption profiles (i.e., current probes, power consumption level at different loads), environmental factors (temperature, humidity and illumination level) and user behavior profiles (presence sensor detectors) is employed in order to provide an interactive scenario in terms of the management of multiple energy sources. Practical tests have been performed by using an XBee ZigBee network in a meshed configuration connected to an experimental microgrid implemented at the Public University of Navarre (UPNA). The system has been implemented in order to provide cloud-enabled data gathering, sending the required information via web services to a private cloud. These initial results are being scaled with the aim of providing a multi-microgrid communication and control scheme.

Dynamic operating conditions of electric generators are very critical case for smaller and isolated electrical power grids. Induction motors play an effective role in industries. At starting an induction motor, it draws high value of current from the power system supply. This causes a voltage and frequency dips in the electric system, and create problems to the supply generator and its connected loads. As compared to a utility, a stand-alone electric generator is a limited electric power source, from the rotating engine, and supply output power. Therefore, a generator power rating must be enough to start and run its connected motor loads in a convenient way. This paper will analyze the dynamic behavior of a stand-alone generator supplying induction motor, during start-up. In addition, the paper introduces a technique to select the most suitable generator power rating supplying certain electric loads taking in consideration the effect of ambient operating temperature, loads power factor, non-linear loads harmonics, and motor loads combined together. The study ensures safe and reliable operation of the supply generator.

Abstract—This study presents a technique clarifying the effect of ambient air temperature and loads power factor changing from standard values on electric generator power rating. The study introduces an optimized technique for selecting the correct electric generator power rating for certain application and operating site ambient temperature. The de-rating factors due to the previous effects will be calculated to be applied on a generator to select its power rating accurately to avoid unsafe operation and save its lifetime. The information in this paper provides a simple, accurate, and general method for synchronous generator selection and eliminates common errors.

Non-linear loads connected to an electric power system produce Harmonic currents, harmonics are introduced into the system in the form of currents whose frequencies are the integral multiples of the fundamental power system frequency (50/60 Hz). The harmonic currents interact with the supply system impedance causing distortions in supply output voltage and current, which has a very bad effect on all other loads connected to the system and the power supply itself, such as overheating, increasing powers losses in the system, and malfunction of protection and control devices connected to the system. This paper presents a study to analyze the effect of voltage and current harmonics resulting from non-linear loads such as variable frequency drive, uninterruptable power supply, and battery chargers on operation and power rating of synchronous generator. The study introduces an optimized method for selecting the suitable generator power rating to withstand harmful harmonics effects for a safe operation of the generator, saving its lifetime, and to improve the power quality of the power system. The method depends on analyzing the effect of increasing the supply generator power rating on the THVD produced from non-linear loads harmonics connected to the system. By calculating the THVD for each case of a generator power rating, a mathematical relationship between generator power rating and TVHD can be found. So, the relationship between generator power rating and total harmonic distortion in the power system will be discussed clearly.