Optimized FPGA implementation of PWAM-based control of three - phase nine - level quasi impedance source inverter

Inherent buck-boost capability, reduced component count, controlled power injection and multilevel operation are some of the advantages which makes cascaded qZSI popular for integrating the generated solar energy with the utility grid. Phase-Shifted Carrier PWM (PSCPWM) and Pulse Width Amplitude Modulation (PWAM) are the most popular techniques for achieving multilevel qZSI operation. Generally, closed loop control implementation of three - phase qZSI system consists of large number of slave controllers (placed locally for voltage control) and one centralized master controller (for grid integration or load current control). Since the aim is to control single system with this highly distributed control structure, issues of clock pulse and interrupt signal synchronization, hardware and software redundancy are common in these implementations. This limits the utilization factor and step size of these control boards. To address these issues, either more optimized solutions must be suggested, or distribution of control structure must be reduced. In this paper, closed loop control of nine - level three - phase qZSI system is implemented using single FPGA control board thereby eliminating above said problems. Since, PWAM control algorithm is more complex than PSCPWM, FPGA based implementation for PWAM control is discussed. Critical implementation processes consisting of DAC - ADC interfacing, FPGA code per unitization, PI Controller realization and different clock pulse utilization are presented. For highlighting and comparing the resource consumption, PWAM and PSCPWM modulation are compared in terms of device utilization. Transient analysis and control algorithm are presented and validated during both starting and load transient conditions by means of simulation results. Finally, hardware results of these modulation methods are discussed and analyzed. 2019 IEEE.This work was supported in part by the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP-EP X-033-2-007, and in part by the Qatar National Library, Doha, Qatar.Scopu

Finite control set model predictive control for grid-tied quasi-Z-source based multilevel inverter

In this paper, a finite control set Model Predictive Control (MPC) for grid-tie quasi-Z-Source (qZS) based multilevel inverter is proposed. The proposed Power Conditioning System (PCS) consists of a single-phase 2-cell Cascaded H-Bridge (CHB) inverter where each module is fed by a qZS network. The aim of the proposed control technique is to achieve grid-tie current injection, low Total Harmonic Distortion (THD) current, unity power factor, while balancing DC-link voltage for all qZS-CHB inverter modules. The feasibility of this strategy is validated by simulation using Matlab/Simulink environment

Dimmable LED Driver For DC Distributed Lighting System

This Abstract proposes a high brightness, high efficiency, dimmable LED driver based on linear current regulator technology for DC grid distribution systems. The proposed driver has excellent characteristics like the highest lumen per watt, long lifetime, high reliability, compact, low cost, both environmental and user-friendly which makes it suitable for lighting applications. Steady-state and small-signal models of the proposed driver is performed which helps in minimizing ground current and accurate compensator design, respectively. These two modeling approaches result in the optimization of both the footprint and cost of the driver. The performance of the proposed Experimental prototype of the 20W driver developed to validate the performance at different dimming levels and achieves maximum efficiency of 97%. the applications of the proposed converter are: dimmable home lighting system, advertisement boards and hoardings, industrial lighting systems, road transport indication lamps, indoor and outdoor stadium lighting, automatic street lighting, decorative systems, health appliances and auditoriums and many mor

Enhanced Deadbeat Control Approach for Grid-Tied Multilevel Flying Capacitors Inverter

This paper proposes an enhanced Deadbeat Controller (DBC) for a grid-tied Flying Capacitors Inverter (FCI). The proposed DBC guarantees the balancing of the capacitors' voltages while injecting current to the grid with lower Total Harmonics Distortion (THD). The proposed controller has the following advantages: 1) Improved current tracking quality even at zero crossing instants by using a weighted state-space model, 2) Superior steady-state performance (lower current THD) compared to other prediction-based control techniques such as Finite-Control-Set Model Predictive Control, 3) The generated duty cycles are normalized to the common base when the desired state is out of reach within the sampling time, 4) Voltage Ride-Through (VRT) capability, and 5) Robustness to parameters variation. Theoretical analysis, simulation, and experimental results are presented to show the effectiveness of the proposed control technique in ensuring uninterruptible and smooth transfer of energy to the grid during normal/abnormal operating conditions (severe voltage sags, parameters variation, etc.)

Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers

Based on the global greenhouse gas (GHG) emissions targets, governments all over the world are speeding up the adoption of electric vehicles (EVs). However, one of the key challenges in designing the novel EV system is to forecast the accurate time for the replacement of conventional vehicles and optimization of charging vehicles. Designing the charging infrastructure for EVs has many impacts such as stress on the power network, increase in traffic flow, and change in driving behaviors. Therefore, the optimal placement of charging stations is one of the most important issues to address to increase the use of electric vehicles. In this regard, the purpose of this study is to present an optimization method for choosing optimal locations for electric car charging stations for Campus charging over long-term planning. The charger placement problem is formulated as a complex Multi-Criteria Decision Making (MCDM) which combines spatial analysis techniques, power network load flow, traffic flow models, and constrained procedures. The Analytic Hierarchy Process (AHP) approach is used to determine the optimal weights of the criteria, while the mean is used to determine the distinct weights for each criterion using the AHP in terms of accessibility, environmental effect, power network indices, and traffic flow impacts. To evaluate the effectiveness of the proposed method, it is applied to a real case study of Qatar University with collected certain attributes data and relevant decision makers as the inputs to the linguistic assessments and MCDM model. The Ranking of the optimal locations is done by aggregating four techniques: Simple Additive Weighting Method (SAW, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II). A long-term impact analysis is a secondary output of this study that allows decision-makers to evaluate their policy impacts. The findings demonstrate that the proposed framework can locate optimal charging station sites. These findings could also help administrators and policymakers make effective choices for future planning and strategy

Planning and Optimizing Electric-Vehicle Charging Infrastructure Through System Dynamics

One of the key solutions to address the issue of energy efficiency and sustainable mobility is to integrate plug-in electric vehicle (EV) infrastructure and photovoltaic (PV) systems. The research proposes a comprehensive EV infrastructure planning and analysis tool (EVI-PAT) with solar power generation for micro-scale projects for the deployment of EV Charging Stations (EVCS). For the evaluation of the proposed infrastructure, a case study of Qatar University (QU) campus is chosen for the integration of the EV charging infrastructure and PV power generation to evaluate the performance of the presented framework. The model estimates the EV adoption and the number of vehicles based on the inputs related to the country's EV adoption, campus vehicle count, and driving behavior. Economic and environmental indicators are used for evaluating policy choices. The findings in the paper show that the proposed planning framework can find the optimum staging plan for EV and PV infrastructure based on the policy choices. The staging plan optimizes the sizes and times of installing EVCSs combined with solar PV keeping the EV-PV project at maximum economic and environmental targets. The optimum policy can affect the optimum power infrastructure limit to maximize the economic benefit by the solar tariff.10.13039/100019779-Qatar National Librar

Differential Flatness-Based Performance Enhancement of a Vector Controlled VSC with an LCL-Filter for Weak Grids

In this paper, a novel single-loop flatness-based controller (FBC) is proposed to control the grid-side current in a shunt converter connected to a weak grid through an LCL-filter. After its mathematical description, the paper reports controller implementation and some performance comparisons with two distinct implementations of the widely diffused vector current control approach, during balanced and unbalanced grid voltages, and weak grid conditions. Obtained results highlight higher tracking capability and better dynamic response of the proposed FBC. Moreover, because of its reduced negative conductance region, unstable behaviors that can be observed in weak grids appear significantly improved due to a reduced influence of the phase-locked loop system

A battery health monitoring method using machine learning: A data-driven approach

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures

A neutral point voltage balancing method for multi-level GTO inverters

The balancing problem of the DC-link capacitor voltage is inherent in the NPC inverter. It is extremely important to keep the upper and lower DC-link capacitor voltages balanced to guarantee the three level operation of the inverter. Generally, the balancing of the DC-link voltage degrades at very low operating frequencies of the inverter. Most proposed methods of neutral point balancing techniques result in an increase of the switching losses of the inverter. To solve this problem a new energy saving PWM method is proposed. This results in a significant reduction of the fluctuations of the neutral point voltage of NPC inverters. Furthermore, the proposed method incorporates techniques to avoid the inherent minimum ON-OFF pulse width limitation in a GTO, without increasing the switching losses of the devices. Theoretical and practical aspects as well as the experimental results are presented in this paper. © 2006 IEEE