Dhiman Chowdhury

In this paper, we present a switching model of a Modular Multilevel Converter (MMC) with half-bridge sub-modules for use in real-time simulations of power electronic systems with high time fidelity. This model is formulated for solving under the Latency Based Linear Multi-step Compound (LB-LMC) simulation method and utilizes a fast algorithm for handling anti-parallel diode conduction across switching elements without use of linearization, averaging, or iterative solving. All four possible conduction modes of switching modules (inserted, bypassed, open, short) are handled by this algorithm. The proposed MMC model is incorporated as a reusable circuit component into the Open Real-Time Simulation (ORTiS) framework, a set of open-source tools for code generating high-performance real-time circuit simulation solvers for FPGA or CPU execution. With modern FPGA implementation using high-level synthesis, the MMC model can real-time simulate with time steps of 26 to 32 nanoseconds for 2-128 switching modules, as shown in presented results.

Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications – device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.

This article presents an efficient design architecture of an islanded and scalable DC microgrid comprising several power management units (PMUs) and a time slot‐based control interface. There are two primary sections in this manuscript—performance analysis of a microgrid design with phase shift modulation (PSM)‐controlled dual active bridge (DAB) converter‐based PMUs to charge Li‐ion battery energy systems and design of a time slot‐based control scheme to regulate the operation of PMUs for future applications. The batteries can act as the power sources in home energy management systems, transactive energy systems and so on. The PMUs are connected in parallel to a capacitive bus, which is supplied power by a photovoltaic (PV) energy source. There is a maximum power point tracking (MPPT) control block, which is constituted by a perturb and observe (P & O) algorithm and boost converter average model. For performance evaluations, the DAB‐based microgrid design is developed and tested in MATLAB/Simulink [textregistered]. In the second section, the control interface design is illustrated and explained with the constituent algorithms. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper articulates design and performance analysis of digital controllers in discrete and continuous time domains for a single-joint robot arm manipulator. The investigated robot arm system is modeled as a single degree of freedom (DOF) plant and there is a feedback sensor implying a closed-loop system. The design approach incorporates discrete (z-plane) and continuous-time (warped s-plane or w-plane) domain parameters. Four digital controllers - phase-lag, phase-lead, proportional-integral (PI) and proportional-integral-derivative (PID) are theoretically designed and implemented to achieve a phase margin of 40 deg. for the compensated system. For performance evaluations, Bode plots of the compensated open-loop systems and step response characteristics of the closed-loop systems are determined.

Electrical network frequency (ENF) is the signature of a power distribution grid which represents the nominal frequency (50 or 60 Hz) of a power system network. Due to load variations in a power grid, ENF sequences experience fluctuations. These ENF variations are inherently located in a multimedia signal which is recorded close to the grid or directly from the mains power line. Therefore, a multimedia recording can be localized by analyzing the ENF sequences of that signal in absence of the concurrent power signal. In this paper, a novel approach to analyze location forensics using ENF sequences extracted from a number of power and audio recordings is proposed. The digital recordings are collected from different grid locations around the world. Potential feature components are determined from the ENF sequences. Then, a multi-class support vector machine (SVM) classification model is developed to validate the location authenticity of the recordings. The performance assessments affirm the efficacy of the presented work.

Using a single-point sensor, non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components. To classify devices, potential features need to be extracted from the electrical signatures. In this article, a novel features extraction method based on current shapelets is proposed. Time-series current shapelets are determined from the normalized current data recorded from different devices. In general, shapelets can be defined as the subsequences constituting the most distinguished shapes of a time-series sequence from a particular class and can be used to discern the class among many subsequences from different classes. In this work, current envelopes are determined from the original current data by locating and connecting the peak points for each sample. Then, a unique approach is proposed to extract shapelets from the starting phase (device is turned on) of the time-series current envelopes. Subsequences windowed from the starting moment to a few seconds of stable device operation are taken into account. Based on these shapelets, a multi-class classification model consisting of five different supervised algorithms is developed. The performance evaluations corroborate the efficacy of the proposed framework.

Electrical network frequency (ENF) is a signature of a power distribution grid. It represents the deviation from the nominal frequency (50 or 60 Hz) of a power system network. The variations in ENF sequences within a grid are subject to load fluctuations within that particular grid. These ENF variations are inherently located in a multimedia signal, which is recorded close to the grid or directly from the mains power line. Thus, the specific location of a recording can be identified by analyzing the ENF sequences of the multimedia signal in absence of the concurrent power signal. In this article, a novel approach to location-stamp authentication based on ENF sequences of digital recordings is presented. ENF patterns are extracted from a number of power and audio signals recorded in different grid locations across the world. The extracted ENF signals are decomposed into low outliers and high outliers frequency segments and potential feature vectors are determined for these ENF segments by statistical and signal processing analysis. Then, a multi-class support vector machine (SVM) classification model is developed to verify the location-stamp information of the recordings. The performance evaluations corroborate the efficacy of the proposed framework.

This paper documents a design and modelling of a grid-connected emergency back-up power supply for medium power applications. There are a rectifier-link boost derived battery charging circuit and a 4-switch push-pull power inverter circuit which are controlled by pulse width modulation (PWM) signals. This paper presents a state averaging model and Laplace domain transfer function of the charging circuit and a switching converter model of the power inverter circuit. A changeover relay based transfer switch controls the power flow towards the utility loads. During off-grid situations, loads are fed power by the proposed inverter circuit and during on-grid situations, battery is charged by an ac-link rectifier-fed boost converter. There is a relay switching circuit to control the charging phenomenon of the battery. The proposed design has been simulated in PLECS and the simulation results corroborate the reliability of the presented framework.

DC microgrids conform to distributed control of renewable energy sources which ratifies efficacious instantaneous power sharing and sustenance of energy access among different domestic Power Management Units (PMUs) along with maintaining stability of the grid voltage. In this paper design metrics and performance evaluation of a scalable DC microgrid are documented where a look-up table of generated power of a source converter complies with the distribution of efficient power sharing phenomenon among a set of two home PMUs. The source converter is connected with a Photovoltaic panel of 300 W and uses Perturb and Observation (P&O) method for executing Maximum Power Point Tracking (MPPT). A boost average DCDC converter topology is used to enhance the voltage level of the source converter before transmission. The load converter consists of two parallely connected PMUs each of which is constructed with high switching frequency based Full Bridge (FB) converter to charge an integrated Energy Storage System (ESS). In this paper the overall system is modeled and simulated on MATLAB/Simulink platform with ESSs in the form of Lead Acid batteries connected to the load side of the FB converter circuits and these batteries yield to support marginalized power utilities. The behaviour of the system is tested in different solar insolation levels along with several battery charging levels of 12 V and 36 V to assess the power efficiency. In each testbed the efficiency is found to be more than 93% which affirm the reliability of the framework and a look-up table is generated comprising the grid and load quantities for effective control of power transmission.