http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

Machine learning in photovoltaic systems: A review

Microelectromechanical systems (MEMS) powered by conventional batteries are disadvantaged in terms of scope of application and environmental friendliness because their power sources need to be replaced regularly and have the risk of polluting the environment. Piezoelectric technology provides a solution for harvesting clean energy such as wind energy from the environment, achieving self-supply of electrical energy for MEMS, and meeting the requirement of without management and maintenance after installation. An overview of piezoelectric wind energy harvesting can help to connect the MEMS field and meet its self-supply needs. This paper presents a comprehensive review of state-of-the-art advances in piezoelectric wind energy harvesters (PWEH). The classification of piezoelectric materials is briefly introduced. The principle of its operation is introduced. It is divided into five categories by structure: bluff body, airfoil, flag, wind concentrator, and wind turbine structures. The research status of harvester has been discussed from four perspectives: structure, application, theoretical modeling, and signal processing. The existing literature is integrated, and the future development directions in three aspects: expanding application area, improving adaptive capabilities, and application-oriented system design are proposed. This paper provides references for people in the industry who are committed to structural innovation and performance enhancement. 

A review of piezoelectric energy harvesters for harvesting wind energy

This paper presents a novel and generic framework for the recognition of emotions using human body expression like head, hand and leg movements. Whole body movements are among the main visual stimulus categories that are naturally associated with faces and the neuro scientific investigation of how body expressions are processed has entered the research agenda this last decade. The database was composed of 254 whole body expressions from 46 actors expressing four emotions (anger, fear, happiness, and sadness). In all pictures the face of the actor was blurred and participants were asked to categorize the emotions expressed in the stimuli in a four alternative-forced-choice task. Using Deep Convolutional Neural Network (DCNN), the input images are trained and modeled. Then the model can be tested by test images for recognizing human emotion from non-verbal communication.

Image fusion and enhancement based on energy of the pixel using Deep Convolutional Neural Network

Power quality (PQ) has become an important topic in today’s power system scenario. PQ issues are raised not only in normal three-phase systems but also with the incorporation of different distributed generations (DGs), including renewable energy sources, storage systems, and other systems like diesel generators, fuel cells, etc. The prevalence of these issues comes from the non-linear features and rapid changing of power electronics devices, such as switch-mode converters for adjustable speed drives and diode or thyristor rectifiers. The wide use of these fast switching devices in the utility system leads to an increase in disturbances associated with harmonics and reactive power. The occurrence of PQ disturbances in turn creates several unwanted effects on the utility system. Therefore, many researchers are working on the enhancement of PQ using different custom power devices (CPDs). In this work, the authors highlight the significance of the PQ in the utility network, its effect, and its solution, using different CPDs, such as passive, active, and hybrid filters. Further, the authors point out several compensation strategies, including reference signal generation and gating signal strategies. In addition, this paper also presents the role of the active power filter (APF) in different DG systems. Some technical and economic considerations and future developments are also discussed in this literature. For easy reference, a volume of journals of more than 140 publications on this particular subject is reported. The effectiveness of this research work will boost researchers’ ability to select proper control methodology and compensation strategy for various applications of APFs for improving PQ.

/pdf/a-comprehensive-survey-on-different-control-strategies-and-ao660ne2vm.pdf

A Comprehensive Survey on Different Control Strategies and Applications of Active Power Filters for Power Quality Improvement

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/rpg2.12119

Piezoelectric energy harvester converting wind aerodynamic energy into electrical energy for microelectronic application

This paper presents a bidirectional on-board single phase electric vehicle charger with a primary aim to achieve Vehicle to Grid (V2G) and Grid to Vehicle (G2V) application. The bidirectional charger configuration mainly consists of a buck converter and a high gain boost converter setup. The battery charging and discharging is based on the state of charge of the battery. Simulation is carried out using matlab simulink software to validate bidirectional on-board charger capabilities with high gain boost converter.

Bidirectional On-Board Single Phase Electric Vehicle Charger with High Gain Boost Converter for V2G Application

Twentieth century has witnessed a tremendous growth in the share of renewable resources in the power grid. Along with the various benefits, this has also raised several technical challenges as well as concerns in system operation, control and protection. These concerns have led to the introduction of advanced metering and protection equipments such as Phasor Measurement Units (PMUs). The highly sampled ‘Big Data’ recorded by PMUs contain significant information about the health of the system. Efficient and timely analysis of this data can address most of the power system concerns such as voltage stability, power system modeling, fault event monitoring, unintentional islanding, state estimation etc. This paper presents a method to detect unintentional islanding using machine learning technique on PMU data. A grid connected PV system is simulated in SIMULINK for data generation. Machine Learning algorithm is adapted to train and further test the data. The results show very good accuracy on test data.

Machine Learning Based Synchrophasor Data Analysis for Islanding Detection

In the past two decades there is a drastic improvement in the technology. The most common word is IOT (Internet of Things) which leads to improvement of Home automation. Switching ON the appliances at home and forgetting to turn them OFF leads to an increase in the daily power consumption. This paper presents the controlling of home appliances on the basis of pre-specified time which is defined from the user’s device; Arduino Ethernet shield, real-time clock, sensors and MOSFET keeping the internet as the medium of communication. This enables the user to monitor the energy usage and assists in minimizing the energy wastage. This prototype is designed for a low cost such that it satisfies the need of different classes of people by notifying them about their consumption by the means of an alert message. The message is sent when power consumption exceeds the limit set by the user making it more user-friendly. An additional task performed by the system is to isolate the load during the fault condition.

Efficient Energy Utilization Control with Remote Network Access

Wireless Power Transfer (WPT) attracts more interest in recent years. This paper deals with design and analysis, of WPT using series resonant full bridge converter for defence application. In this paper, inductive coupling method is used to transfer 100W of power wirelessly for the distance of 3mm to lcm. Here, the complete design of resonant tank and load factor has been explained and simulation analysis of both open loop and closed loop is carried out and compared the results using LTSPICE software.

Wireless power transfer using resonant converters for Defence application

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