ABSTRACT
Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level. It involves the active participation of consumers in load management through different incentives. To enable the consumers for efficient energy management, it is important to provide them information about the energy consumption patterns of their appliances. Appliance load monitoring (ALM) is a feedback system used for providing feedback to customers about their power consumption of individual appliances. For accessing appliance power consumption, the determination of the operating status of various appliances through feedback systems is necessary. Two major approaches used for ALM are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). In this paper, a hybrid adaptive-neuro fuzzy inference system (ANFIS) is used as an application for NILM. ANFIS model being sophisticated was difficult to work with, but ANFIS model helps to achieve better results than other competent approaches. An ANFIS system is developed for extracting appliance features and then a fine tree classifier is used for classifying appliances having more than 1 kW power rating based on the extracted feature. Several case studies have been performed using ANFIS on a publicly available United Kingdom Domestic Appliance Level Electricity (UK-Dale dataset). The simulation results obtained from the ANFIS for NILM are compared with relevant literature to show the performance of the proposed technique. The results prove that the novel application of ANFIS gives better performance for solving the NILM problem as compared to the other existing techniques.
ABSTRACT
This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.
ABSTRACT
The friction welding of tube to tube plate using an external tool (FWTPET) is widely deployed in several industrial applications, such as aerospace, automotive, and power plants. Moreover, for achieving a better tensile strength and hardness in the weld zone, the friction stir processing (FSP) technique was incorporated into the FWTPET process for joining aluminum alloys (AA6063 tube, AA6061 tube plate). Furthermore, it has to be noted that FWTPET was applied for joining the AA6063 tube to the AA6061 tube plate, and FSP was deployed for reinforcing the weld zone with carbon nanotube (CNT) and silicon nitride (Si3N4) particles, thereby attaining the desirable mechanical properties. Subsequently, the Taguchi L25 orthogonal array was used for identifying the most influential input and output FWTPET + FSP process parameters. Furthermore, particle swarm optimization (PSO) and the firefly algorithm (FFA) were deployed for determining the optimized input and output FWTPET + FSP process parameters. The input process parameters include CNT, Si3N4, rotational tool speed, and depth. Furthermore, the tensile strength of the welded joint was considered as the output process parameter. The process parameters predicted by PSO and FFA were compared with the experimental values. It was witnessed that deviation between the predicted and experimental values was minimal. Moreover, it was found that FFA provided a superior tensile strength prediction than PSO.
