This paper proposes a method for power flow control between utility and microgrid through back-to-back converters, which facilitates desired real and reactive power flow between utility and microgrid. In the proposed control strategy, the system can run in two different modes depending on the power requirement in the microgrid. In mode-1, specified amount of real and reactive power are shared between the utility and the microgrid through the back-to-back converters. Mode-2 is invoked when the power that can be supplied by the DGs in the microgrid reaches its maximum limit. In such a case, the rest of the power demand of the microgrid has to be supplied by the utility. An arrangement between DGs in the microgrid is proposed to achieve load sharing in both grid connected and islanded modes. The back-to-back converters also provide total frequency isolation between the utility and the microgrid. It is shown that the voltage or frequency fluctuation in the utility side has no impact on voltage or power in microgrid side. Proper relay-breaker operation coordination is proposed during fault along with the blocking of the back-to-back converters for seamless resynchronization. Both impedance and motor type loads are considered to verify the system stability. The impact of dc side voltage fluctuation of the DGs and DG tripping on power sharing is also investigated. The efficacy of the proposed control ar-rangement has been validated through simulation for various operating conditions. The model of the microgrid power system is simulated in PSCAD.

Power management and power flow control with back-to-back converters in a utility connected microgrid

Health monitoring systems with low-cost sensor networks and smart algorithms are always needed in both passenger trains and heavy haul trains due to the increasing need for reliability and safety in the railway industry. This paper focuses on an overview of existing approaches applied for railway vehicle on-board health monitoring systems. The approaches applied in the data measurement systems and the data analysis systems in railway on-board health monitoring systems are presented in this paper, including methodologies, theories and applications. The pros and cons of the various approaches are analysed to determine appropriate benchmarks for an effective and efficient railway vehicle on-board health monitoring system. According to this review, inertial sensors are the most popular due to their advantages of low cost, robustness and low power consumption. Linearisation methods are required for the model-based methods which would inevitably introduce error to the estimation results, and it is time-...

An overview: modern techniques for railway vehicle on-board health monitoring systems

In the paradigm of the increasing trend towards decarbonisation, the use of sustainable renewable energy is widely recommended. Network reconfiguration, together with the incorporation of battery energy storage systems (BESS), facilitates the integration of renewable power and enhances the loadability of the system, leading to prolonged utilization of the existing equipment. Optimized allocation of PV-DG and BESS is implemented using particle swarm optimization (PSO) in the present work. Dynamic hourly and static seasonal reconfiguration with optimally allocated PV-DG and BESS is presented. Moreover, static annual reconfiguration, followed by the optimal apportionment of distributed resources, is achieved. The reconfiguration objectives considered are loss minimization as well as voltage, and loadability improvement. Hourly and seasonal variations in load and PV-DG generated power are taken into account. Additionally, ‘P’ and ‘PQV’ buses are incorporated in the load flow algorithm used in reconfiguration to facilitate remote voltage regulation. A comparison of the net annual energy savings is made among the three types of reconfiguration for three scenarios, namely, a system with PV-DG only, a system with PV-DG and BESS, and a system with PV-DG, BESS and P and PQV buses.

Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system

Greenhouse gas emissions from the freight transportation sector are a significant contributor to climate change, pollution, and negative health impacts because of the common use of heavy-duty diesel vehicles (HDVs). Governments around the world are working to transition away from diesel HDVs and to electric HDVs, to reduce emissions. Battery electric HDVs and hydrogen fuel cell HDVs are two available alternatives to diesel engines. Each diesel engine HDV, battery-electric HDV, and hydrogen fuel cell HDV powertrain has its own advantages and disadvantages. This work provides a comprehensive review to examine the working mechanism, performance metrics, and recent developments of the aforementioned HDV powertrain technologies. A detailed comparison between the three powertrain technologies, highlighting the advantages and disadvantages of each, is also presented, along with future perspectives of the HDV sector. Overall, diesel engine in HDVs will remain an important technology in the short-term future due to the existing infrastructure and lower costs, despite their high emissions, while battery-electric HDV technology and hydrogen fuel cell HDV technology will be slowly developed to eliminate their barriers, including costs, infrastructure, and performance limitations, to penetrate the HDV market.

A Review of Heavy-Duty Vehicle Powertrain Technologies: Diesel Engine Vehicles, Battery Electric Vehicles, and Hydrogen Fuel Cell Electric Vehicles

Recent advances in wireless sensor networking (WSN) techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems. These have the potential for use in the monitoring of railway signaling systems and rail tracks. Energy efficiency is one of the most important design factors for the WSNs as the typical sensor nodes are equipped with limited power batteries. In earlier research, an energy-efficient cluster-based adaptive time-division multiple-access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed by the authors for the purpose of communication between the sensors placed in a railway wagon. This paper proposes another new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period. In addition to railway applications, the EA-TDMA and E-BMA protocols are suitable for generic wireless data communication purposes. Both analytical and simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented in this paper to demonstrate the superiority of the EA-TDMA and E-BMA protocols.

Energy-Efficient Wireless MAC Protocols for Railway Monitoring Applications

The commercialization of hydrogen as a fuel faces severe technological, economic, and environmental challenges. As a method to overcome these challenges, microalgal biohydrogen production has become the subject of growing research interest. Microalgal biohydrogen can be produced through different metabolic routes, the economic considerations of which are largely missing from recent reviews. Thus, this review briefly explains the techniques and economics associated with enhancing microalgae-based biohydrogen production. The cost of producing biohydrogen has been estimated to be between $10 GJ-1 and $20 GJ-1, which is not competitive with gasoline ($0.33 GJ-1). Even though direct biophotolysis has a sunlight conversion efficiency of over 80%, its productivity is sensitive to oxygen and sunlight availability. While the electrochemical processes produce the highest biohydrogen (>90%), fermentation and photobiological processes are more environmentally sustainable. Studies have revealed that the cost of producing biohydrogen is quite high, ranging between $2.13 kg-1 and 7.24 kg-1 via direct biophotolysis, $1.42kg-1 through indirect biophotolysis, and between $7.54 kg-1 and 7.61 kg-1 via fermentation. Therefore, low-cost hydrogen production technologies need to be developed to ensure long-term sustainability which requires the optimization of critical experimental parameters, microalgal metabolic engineering, and genetic modification.

/pdf/biohydrogen-production-from-biomass-sources-metabolic-2osbm072mr.pdf

Biohydrogen Production From Biomass Sources: Metabolic Pathways and Economic Analysis

In the past five years substantial amounts of rooftop photovoltaic (PV) generation have been installed across the low voltage distribution networks. These networks often have design lifetimes of 40 years. Most of the existing distribution networks were constructed, and the voltage profiles set, prior to the rapid uptake of PV. When power quality difficulties arise reconstruction or some other form of intervention is required. This review will focus on power electronic options for voltage profile management in networks with high penetrations of PV. A voltage profile improvement can be achieved with a range of series or shunt connected devices. The traditional voltage regulator adds a series compensation voltage and electronic and electro-mechanical variations exist. Shunt devices attempt to influence voltage with reactive or real power flows when storage is allowed. A shunt device is additionally capable of phase balancing which may substantially raise the allowable PV fraction. In addition to the topological choices, the converter choice will impact upon capability. This paper will examine full converter solutions which incorporate meaningful energy storage at a DC bus and reduced energy storage scenarios or storage free solutions such as matrix converter (MC) based devices.

A review of topological ordering based voltage rise mitigation methods for LV distribution networks with high levels of photovoltaic penetration

The availability of low-powered and cheap microprocessors, radio frequency integrated circuits and the development of new wireless communication techniques, make the wireless sensor networks (WSN) one of todays most promising technologies. Minimizing energy consumption and maximizing the lifetime of the networks are key requirements in the design of sensor network applications. Optimally designed medium access control (MAC) and routing protocols minimize energy consumption and prolong the network life. In this study, we have investigated an energy-efficient adaptive TDMA (EA-TDMA) protocol for railway applications that used in communication between sensor nodes and the cluster-head (CH) placed in a railway wagon. This protocol is suitable for medium traffic applications and reduces energy consumption by shortening the idle period when devices have no data to transmit. We have developed an analytical model for EA-TDMA and compared its performance with conventional TDMA and bit-map-assisted (BMA) protocols.

/pdf/energy-efficient-tdma-mac-protocol-for-wireless-sensor-kz97italak.pdf

Energy-efficient TDMA MAC protocol for wireless sensor networks applications

In this paper, a proton exchange membrane fuel cell (PEMFC) is implemented as a grid-connected electrical generator that uses hydrogen gas as fuel and air as an oxidant to produce electricity through electrochemical reactions. Analysis demonstrated that the performance of the PEMFC greatly depends on the rate of fuel supply and air supply pressure. Critical fuel and air supply pressures of the PEMFC are analysed to test its feasibility for the grid connection. Air and fuel supply pressures are varied to observe the effects on the PEMFC characteristics, efficiency, fuel supply, and air consumption over time. The PEMFC model is then implemented into an electrical power system with the aid of power electronics applications. Detailed mathematical modelling of the PEMFC is discussed with justification. The PEMFC functions as an electrical generator that is connected to the local grid through a power converter and a transformer. Modulation of the converter is controlled by means of a proportional-integral controller. The two-axis control methodology is applied to the current control of the system. The output voltage waveform and control actions of the controller on the current and frequency of the proposed system are plotted as well. Simulation results show that the PEMFC performs efficiently under certain air and fuel pressures, and it can effectively supply electrical power to the grid.

https://www.mdpi.com/1996-1073/13/24/6679/pdf

Proton Exchange Membrane Hydrogen Fuel Cell as the Grid Connected Power Generator

Wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle monitoring systems that ensure safe and secure operations of the rail vehicle. To make an energy-efficient WSN application, power consumption due to raw data collection and pre-processing needs to be kept to a minimum level. In this paper, an energy-efficient data acquisition method has investigated for WSN applications using modern machine learning techniques. In an existing system, four sensor nodes were placed in each railway wagon to collect data to develop a monitoring system for railways. In this system, three sensor nodes were placed in each wagon to collect the same data using popular regression algorithms, which reduces power consumption of the system. This study was conducted using six different regression algorithms with five different datasets. Finally the best suitable algorithm have suggested based on the performance metrics of the algorithms that include: correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity.

https://dro.deakin.edu.au/eserv/DU:30059200/shafiullah-reductionpower-2008.pdf

Sawkat Ali

Papers

Biohydrogen Production From Biomass Sources: Metabolic Pathways and Economic Analysis

A review of topological ordering based voltage rise mitigation methods for LV distribution networks with high levels of photovoltaic penetration

Energy-efficient TDMA MAC protocol for wireless sensor networks applications

Proton Exchange Membrane Hydrogen Fuel Cell as the Grid Connected Power Generator

Reduction of power consumption in sensor network applications using machine learning techniques