Evolution engine technology in exhaust gas recirculation for heavy-duty diesel engine

In this present year, engineers have been researching and inventing to get the optimum of less emission in every vehicle for a better environmental friendly. Diesel engines are known reusing of the exhaust gas in order to reduce the exhaust emissions such as NOx that contribute high factors in the pollution. In this paper, we have conducted a study that EGR instalment in the vehicle can be good as it helps to prevent highly amount of toxic gas formation, which NOx level can be lowered. But applying the EGR it can lead to more cooling and more space which will affect in terms of the costing. Throughout the research, fuelling in the engine affects the EGR producing less emission. Other than that, it contributes to the less of performance efficiency when vehicle load is less

A Constant Grid Interface Current Controller for DC Microgrid

With the increased percentage of distributed renewable energy sources (RES) connected to the power network, it is challenging to maintain the balance between the power generation and consumptions against the unpredictable renewable energy generation and load variations. Considering this, this study proposed a new DC microgrid control strategy to reduce the disturbance to the main power grid from the distributed generation and load variations within the DC microgrid. The DC microgrid model used in this study includes an energy storage unit (battery), a distributed generation unit (PV) and loads. A fuzzy logic controller (FLC) is used to actively regulate the battery charging/discharging current to absorb the power variation caused by PV generation and load changes. The proposed control strategy is validated by simulation in MATLAB/Simulink

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

Improvement of Dynamic Performance of the Grid Connected Photovoltaic (PV) System by Series Compensating Devices

This study proposes the bridge type fault current limiter (BFCL) and series dynamic braking resistor (SDBR) methodologies to enhance the low voltage ride through (LVRT) capability of the PV plant during network faults. This work also focuses on smoothing out the PV power fluctuation due to variable irradiance. To achieve this, the output power of diversely located identical and small PV plants, instead of a big PV plant in a certain area, is integrated. Simulations performed through the Matlab/Simulink software shows that the terminal voltage obtained from the proposed protection schemes maintains the grid code, and hence the PV systems need not to be disconnected from the grid during fault. It is also seen that smoothing of PV power fluctuation is possible by the proposed integration method. Also, the size of the energy storage required for the diversely located PV system is smaller than that for a big PV plant

A novel power management and control design framework for resilient operation of microgrids

This thesis concerns the investigation of the integration of the microgrid, a form of future electric grids, with renewable energy sources, and electric vehicles. It presents an innovative modular tri-level hierarchical management and control design framework for the future grid as a radical departure from the ‘centralised’ paradigm in conventional systems, by capturing and exploiting the unique characteristics of a host of new actors in the energy arena - renewable energy sources, storage systems and electric vehicles. The formulation of the tri-level hierarchical management and control design framework involves a new perspective on the problem description of the power management of EVs within a microgrid, with the consideration of, among others, the bi-directional energy flow between storage and renewable sources. The chronological structure of the tri-level hierarchical management operation facilitates a modular power management and control framework from three levels: Microgrid Operator (MGO), Charging Station Operator (CSO), and Electric Vehicle Operator (EVO). At the top level is the MGO that handles long-term decisions of balancing the power flow between the Distributed Generators (DGs) and the electrical demand for a restructure realistic microgrid model. Optimal scheduling operation of the DGs and EVs is used within the MGO to minimise the total combined operating and emission costs of a hybrid microgrid including the unit commitment strategy. The results have convincingly revealed that discharging EVs could reduce the total cost of the microgrid operation. At the middle level is the CSO that manages medium-term decisions of centralising the operation of aggregated EVs connected to the bus-bar of the microgrid. An energy management concept of charging or discharging the power of EVs in different situations includes the impacts of frequency and voltage deviation on the system, which is developed upon the MGO model above. Comprehensive case studies show that the EVs can act as a regulator of the microgrid, and can control their participating role by discharging active or reactive power in mitigating frequency and/or voltage deviations. Finally, at the low level is the EVO that handles the short-term decisions of decentralising the functioning of an EV and essential power interfacing circuitry, as well as the generation of low-level switching functions. EVO level is a novel Power and Energy Management System (PEMS), which is further structured into three modular, hierarchical processes: Energy Management Shell (EMS), Power Management Shell (PMS), and Power Electronic Shell (PES). The shells operate chronologically with a different object and a different period term. Controlling the power electronics interfacing circuitry is an essential part of the integration of EVs into the microgrid within the EMS. A modified, multi-level, H-bridge cascade inverter without the use of a main (bulky) inductor is proposed to achieve good performance, high power density, and high efficiency. The proposed inverter can operate with multiple energy resources connected in series to create a synergized energy system. In addition, the integration of EVs into a simulated microgrid environment via a modified multi-level architecture with a novel method of Space Vector Modulation (SVM) by the PES is implemented and validated experimentally. The results from the SVM implementation demonstrate a viable alternative switching scheme for high-performance inverters in EV applications. The comprehensive simulation results from the MGO and CSO models, together with the experimental results at the EVO level, not only validate the distinctive functionality of each layer within a novel synergy to harness multiple energy resources, but also serve to provide compelling evidence for the potential of the proposed energy management and control framework in the design of future electric grids. The design framework provides an essential design to for grid modernisation

Measurements, Models, Systems and Design

531 s.

Design and analysis of a novel multi-input multi-output high voltage DC transformer model

a novel Multi-Input Multi-Output (MIMO) step-up DC transformer for applications in high voltage renewable energy sources is designed and presente

Parallel Distributed Compensation-PID Controller Design for Maximum Power Point Tracking of Dynamic Loaded Photovoltaic System

Control issues come from the output voltage of PV installations and systems operating in a range of irradiance and temperature. By using a DC converter, such systems are able to maintain a constant output voltage despite fluctuations in the generated voltage and load. The design of a maximum power point tracking (MPPT) on DC converter controller is presented in this article for a system. Fractional Order-Proportional Integral Derivative (FO-PID) and Parallel Distributed Compensation-Proportional Integral Derivative (PDC-PID) controllers have been implemented to the system converter as a proposed control approach. Particle Swarm Optimization (PSO) is used as optimization technique for determining the optimal parameters of (FO-PID) and (PDC-PID) controllers for tracking the output voltage from trained Adaptive Neuro Fuzzy Inference System (ANFIS) that is corresponding to maximum power generated from (PV) module. The PV system with the dynamic load is modeled and simulated by using the MATLAB/Simulink environment. The system performance is displayed in the form of a family of curves under different operating conditions