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With the development of the Internet of Things (IoT), Low Data Rate-Personal Area Networks (LR-WPAN) have been deployed for different applications. Now comes the need to integrate these networks in search of greater connectivity, performances, and geographic coverage. This integration is facilitated by the recent deployment of low power wide area networks (LPWAN) in the licensed bands, especially narrowband IoT (NB-IoT) and long-term evolution for machine-type communications (LTE-M), which are standardized technologies that will continue evolving as part of the fifth generation (5G) specifications. This paper proposes a design methodology for combined networks using LR-WPAN and LPWAN technologies. These networks are combined at the device level using a cluster-tree topology. An example is shown here, where an existing IEEE 802.15.4 network is combined with NB-IoT. To this end, new dual nodes are incorporated, acting as cluster heads. The paper discusses the different aspects of formation and operation of the combined network. A dynamic link selection (DLS) algorithm is also proposed, based on which cluster headers dynamically determine the preferred link, depending on link quality and type of traffic. Extensive simulations show that the DLS algorithm significantly increases battery life on dual nodes, which are the nodes with the highest power demands.

This paper describes the mathematical model for the optimal power flow analysis of the IEEE 14 bus system. In the IEEE 14 bus system were connected two distributed generators (DGs): a hydro generator and a photovoltaic power plant. The two objective functions which are minimized, are the generation costs and the total power losses of the system, while there are satisfied equality and inequality constraints. The optimal power flow analysis will be performed for the steady-state condition of the system and for the whole day, for two cases: the DGs are connected to the system and the DGs are not connected to the system.

It is with great pleasure, PES welcomes your interest to the subscription free publication in the field of power and energy: IEEE Open Access Journal of Power and Energy (OAJPE). Throughout the world, the outcome of research funded through public finance is expected to be freely available to the member of the society. Such expectation very much resonates the core mission of PES publication: dissemination of high-quality research outcome to the wider power and energy community through rigorous peer review process. Electric energy is at the center stage of the modern digitized and decarbonized world. It has now become an integral pathway of modern living and empowerment of people. Educating the community by bringing the latest development in power and energy technology is an effort from the PES, such as free and open access publications.

Power System Stabilizers (PSS) are added to excitation systems, in order to enhance the damping of electric power system during low frequency oscillations. For large scale power systems comprising of many interconnected machines, the PSS parameter tuning is a complex exercise due to the presence of several poorly damped modes of oscillation. The problem is further being complicated by continuous variation in power system operating conditions. In the simultaneous tuning approach, exhaustive computational tools are required to obtain optim um parameter settings for the PSS, while in the case of sequential tuning, although the computational load is fewer, evaluating the tuning sequence is an additional requirement. There is a further problem of eigenvalue drift. This research proposes the PSS model based on IEEE Standard 421.5 PSS 4B for designing robust power system stabilizers for a multi machine system. Simulations were carried out using several fault tests at transmission line on a Two-Area Multimachine Power System. As a reference the PSS model, Delta w PSS and Delta Pa PSS has been used for comparison with the PSS under consideration. The result shows that power transfer response using the model is more robust than Delta w PSS and Delta Pa PSS, especially for three phase faults and phase to ground faults.

This article presents a dataset obtained from the deployment of an IEEE 802.15.4g SUN (Smart Utility Network) single-hop network (11 nodes) in a large industrial scenario (110,044 m 2 ) for a long period of time (99 days). The dataset contains ∼11 M entries with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment), and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in the average RSSI (i.e., between −82.1 dBm and −101.7 dBm) and CCA (i.e., between −111.2 dBm and −119.9 dBm) values, which is caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, these values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are needed to meet the link reliability requirements of industrial applications. Hence, the presented dataset will allow researchers and practitioners to propose novel mechanisms and evaluate their performance using realistic conditions, enabling the dependability vision of the RAW (Reliable and Available Wireless) WG (Working Group) at the IETF (Internet Engineering Task Force).

The battery life of Mobile Stations in IEEE 802.16e can be extended substantially by applying the sleep mode mechanism. This paper studies an effcient method to analyze the performance of the power saving class type II for delay sensitive traffc in multiple sleep state. The incoming data frames may join or balk the buffer due to impatience with some probability. We present an M/M/1/N queueing model with balking and multiple vacations in order to exhibit the self-similar property of IEEE 802.16e. We develop a cost function to determine the optimal service rate that minimizes the total expected cost. Various performance indices such as the average number of data frames in the system, the mean waiting time of the data frame in the system, the the average balking rate due to impatience, etc. have been presented. Numerical results are provided to show the inuence of various parameters on the behavior of the system. The proposed model provides a tradeoff between the average abandon rate and the power consumption.

Este artículo presenta la implementación de un sistema de adquisición y evaluación de fenómenos electromagnéticos asociados con la calidad de la energía según el Estándar IEEE 1159-1992 y aplicado al caso particular de sistemas eléctricos monofásicos. El software de evaluación se implementa en el software profesional Labview®, utilizando como dispositivo de adquisición una tarjeta NI USB 6009 de la National Instrument®. Se muestra la implementación del hardware de la sensórica, también el desarrollo del algoritmo y el diseño de una interfaz gráfica de usuario que permiten visualizar las formas de onda de tensión y de corriente, espectros, frecuencia, potencia y los disturbios de calidad de la energía tales como sag, swell, undervoltage, overvoltage, etc. De igual forma al final se realiza un conciso análisis de costos para demostrar que el sistema desarrollado presenta un precio inferior a las soluciones actuales que se consiguen en el mercado, por último se muestra un estudio de caso.

• Fault classification and localization using extreme learning machine. • Weighted extreme leaning-based fault classification and localization. • Comparative assessment with state-of-the-art classifier. • Robustness assessment against the noisy data. Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively). [ABSTRACT FROM AUTHOR]

Dynamic state estimation (DSE) accurately tracks the dynamics of a power system and provides the evolution of the system state in real-time. This paper focuses on the control and protection applications of DSE, comprehensively presenting different facets of control and protection challenges arising in modern power systems. It is demonstrated how these challenges are effectively addressed with DSE-enabled solutions. As precursors to these solutions, reformulation of DSE considering both synchrophasor and sampled value measurements and comprehensive comparisons of DSE and observers have been presented. The usefulness and necessity of DSE based solutions in ensuring system stability, reliable protection and security, and resilience by revamping of control and protection methods are shown through examples, practical applications, and suggestions for further development. [ABSTRACT FROM AUTHOR]