An IOT based smart metering development for energy management system

The worldwide energy demand is increasing and hence necessity measures need to be taken to reduce the energy wastage with proper metering infrastructure in the buildings. A Smart meter can be used to monitor electricity consumption of customers in the smart grid technology. For allocating the available resources proper energy demand management is required. During the past years, various methods are being utilized for energy demand management to precisely calculate the requirements of energy that is yet to come. A large system presents a potential esteem to execute energy conservation as well as additional services linked to energy services, extended as a competent with end user is executed. The supervising system at the utilities determines the interface of devices with significant advantages, while the communication with the household is frequently proposing particular structures for appropriate buyer-oriented implementation of a smart meter network. Also, this paper concentrates on the estimation of vitality utilization. In this paper energy is measured in units and also product arrangement is given to create bill for energy consumption and implementing in LabVIEW software. An IOT based platform is created for remote monitoring of the metering infrastructure in the real time. The data visualization is also carried out in webpage and the data packet loss is investigated in the remote monitoring of the parameters

Genetic algorithm based reactive power dispatch for voltage stability improvement,

a b s t r a c t Voltage stability assessment and control form the core function in a modern energy control centre. This paper presents an improved Genetic algorithm (GA) approach for voltage stability enhancement. The proposed technique is based on the minimization of the maximum of L-indices of load buses. Generator voltages, switchable VAR sources and transformer tap changers are used as optimization variables of this problem. The proposed approach permits the optimization variables to be represented in their natural form in the genetic population. For effective genetic processing, the crossover and mutation operators which can directly deal with the floating point numbers and integers are used. The proposed algorithm has been tested on IEEE 30-bus and IEEE 57-bus test systems and successful results have been obtained

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user’s control through a user-friendly mobile/web application (APP). The different features impacting the identification of hotspots are considered, and feature selection is performed using various classifier-based learning algorithms, namely, Naive Bayes, Nearest neighbors, Random Forest and Support Vector Machine (SVM). The performance of classifications are compared. From the real-time results, it is clear that the Naive Bayes classification model is found to provide better accuracy, which is employed in the application platform for predicting the potential fishing zone. After identifying the first catch, the species are classified using an AlexNet-based deep Convolutional Neural Network. Also, the user can fetch real-time information such as the status of fishing through live video streaming to determine the quality and quantity of fish along with information like pH, temperature and humidity. The proposed work is implemented in a real-time boat structure prototype and is validated with data from sensors and satellites

Coordination of SRF-PLL and Grid Forming Inverter Control in Microgrid with Solar PV and Energy Storage

Recently, there has been a huge advancement in renewable energy integration in power systems. Power converters with grid-forming or grid-following topologies are typically employed to link these decentralized power sources to the grid. However, because distributed generation has less inertia than synchronous generators, their use of renewable energy sources threatens the electrical grid’s reliability. Suitable control approaches for ensuring frequency and voltage stability in the grid-connected form of operation are established in this study, which offers dynamic, seamless power switching in the islanded mode of operation. In this research, effective Phase Locked Loop (PLL) techniques for grid-forming (GFM) and grid-following (GFL) converters are designed to achieve a smooth transition from grid-tied to islanded mode of operation. In this work, PLL configurations are implemented while considering the active and reactive power, frequency, voltage, and current parameters of the system, and ensuring voltage and frequency stability. The simulation results in a microgrid network that ensures a smooth transition of power transfer while switching between modes of operation, and supports the voltage and frequency stability of the system

Multi-Objective Genetic Algorithm for voltage stability enhancement using rescheduling and FACTS devices

This paper presents the application of Multi-Objective Genetic Algorithm to solve the Voltage Stability Constrained Optimal Power Flow (VSCOPF) problem. Two different control strategies are proposed to improve voltage stability of the system under different operating conditions. The first approach is based on the corrective control in contingency state with minimization of voltage stability index and real power control variable adjustments as objectives. The second approach involves optimal placement and sizing of multi-type FACTS devices, Static VAR Compensator and Thyristor Controlled Series Capacitor along with generator rescheduling for minimization of voltage stability index and investment cost of FACTS devices. A fuzzy based approach is employed to get the best compromise solution from the trade off curve to aid the decision maker. The effectiveness of the proposed VSCOPF problem is demonstrated on two typical systems, IEEE 30-bus and IEEE 57 bus test systems