In Ecuador, the existence of chronic obstructive pulmonary diseases and the appearance of new viruses such as Covid-19 that cause respiratory problems, influence the development of oxygen therapy services. These services improve the health of some patients and increase life expectancy in others. However, the limited care capacities in the hospital centers of Ecuador and the lack of development of self-sufficient technological equipment for the provision of therapeutic oxygen services, mean that said service is carried out mainly in large cities, leaving aside other geographical areas with populations that also require it. This article explores the implementation of an oxygen therapy system based on the use of alternative energies, to be used in the treatment of people who cannot do without supplemental oxygenation in communes and homes. In addition, a control system based on linguistic rules for oxygen supply is proposed. Finally, the closed loop response of the control system is analyzed.
Brushless DC motors are widely utilised nowadays because of their many benefits, including their quiet operation, low maintenance requirements, and high efficiency. Robotics, medical equipment, autos, ships, aeroplanes, and tanks are just some of the many places you may find brushless DC motors in industry. Due to the unpredictable nature of the environment in such applications, the real model often differs from the nominal one. To put it another way, there are major flaws in the models used and severe external disruptions. There must be a reliable method of control that can solve these problems. When it comes to operating a brushless DC motor system, the conventional PID control approach has a number of drawbacks. A Hybrid Fuzzy-FOPID controller is employed in the current system to regulate the BLDC motor. A modified harmony search (HS) metaheuristic Algorithm is designed for modifying FOPID controller settings. The hybrid fuzzy-FOPID controller that was installed greatly enhances motor speed and torque responsiveness in a number of operating circumstances. In the current system, Hybrid Fuzzy-FOPID has the drawback of having a slightly greater steady-state error, ripples throughout the speed profile, and restricted starting torque in all three operating circumstances. To overcome the drawbacks of the present system, we must employ a BLDC motor with a hybrid ANFIS-FOPID controller. The proposed work was created and implemented in MATLAB/SIMULINK.
The digitalization of wind power generation is rapidly progressing with the development of ICT technologies such as big data, IoT, and artificial intelligence, along with the trend of large-scale wind power generation and the expansion of offshore wind power generation. As the number of wind turbines installed in power generation complexes decreases due to the increasing size of turbines, the availability, reliability, efficiency, and lifespan of each wind turbine are becoming increasingly important. In particular, digital twin technology for high-efficiency operation is expected to accelerate due to the large-scale expansion of offshore wind power and the increase in operation and maintenance (O&M) costs resulting from the scale and aging of turbines. In this paper, we intend to establish a foundation for extending the lifespan of wind turbines through performance analysis and durability evaluation of Korean wind turbines, as well as the development of remaining life prediction technology. To predict the remaining life of a wind turbine, we selected crucial components based on failure rates and downtime, focusing on identifying the core parts of the turbine. Actual SCADA and CMS operational data for key components were acquired, and system performance was analyzed using artificial intelligence and knowledge-based condition diagnosis algorithms. The remaining life prediction program will be enhanced using real-time sensor data obtained from the following performance analysis. The final outcome will be applied to real wind turbines in Korea for a long-term demonstration.
Partial shading (PS) considerably restricts photovoltaic (PV) systems, requiring extraction of the global maximum power point (GMPP). This persistent challenge engenders continuous fluctuations in the maximum power point (MPP) and demands utmost attention. In this regard, this paper presents a novel hybrid scanning technique and a Perturb and Observe (P&O) algorithm meticulously designed to accurately track the PV array's GMPP encountering PS, non-uniform dust deposition, or any common failures. Furthermore, it serves as an efficient tool that operates in tandem with the dynamic reconfiguration approaches. Extensive simulation tests were carried out using MATLAB Simulink software, while the validation and verification processes were conducted using an integrated Arduino board. Consequently, the simulation results exhibit outstanding accuracy and stability.
This research paper presents a grid-connected DC/DC converter with multiple inputs, emphasizing a substantial voltage gain and non-isolated operation. The structure can accommodate a number of input stages and allows independently performing the MPPT function and power sharing. Since a higher voltage increase is obtained in the offered converter with more input sources, there will be no need to connect solar modules in series or parallel to achieve higher voltage and power ratings. The converter inherently offers higher reliability. The converter also draws current with minimal ripple for inputs, which is preferred by solar PV systems. These properties allow using this converter as a suitable and high-quality converter for high power to connect solar PV energy sources to distribution network.
This paper presents a study and a management of an autonomous hybrid microgrid system based on photovoltaic (PV) and wind renewable energy sources (RES). These power systems deliver electricity to remote locations including isolated villages in either desert or mountains, offshore islands, or military bases where it is either technically difficult or economically unfeasible to connect with the main power grid. In microgrid system based on RES, the power generation is heavily reliant to the meteorological conditions. Moreover, the lifespan of the energy storage systems decreases when the number of operating cycle increases. Therefore, one of the most important issues is the ability to provide the load with the necessary power despite the significant variation of the produced energy. Consequently, a robust energy management strategy is of prime importance in these systems. The paper proposes a design and simulation of an energy management strategy that considers various operation modes of an autonomous hybrid microgrid system. Extensive numerical simulations results performed on MATLAB/Simulink for an autonomous PV/wind microgrid feeding 8kW load at various operating conditions validate the proposed strategy.
This paper presents the stable and robust control of Surface Permanent Magnet Synchronous Motor (SPMSM) with Active LC Low Pass Filter (LC-LPF) by IRM-ILQ (Inverse Reference Model - Inverse Linear Quadratic) method. The output voltages of PWM inverters, which used for motor driving contain harmonic components due to its switching. LC-LPF is effective to eliminate those harmonics though, it has two dynamics. Therefore, applying the IRM-ILQ method is necessary since they have two degrees of freedom and can control followability and robustness at the same time. In this paper, the stable and robust control has been confirmed by experiments.
This paper critically reviews the Indicative Generation Capacity Expansion Plan (IGCEP) 2021 of the National Transmission Dispatch Company (NTDC) in Pakistan. It provides a comprehensive analysis of the capacity and energy balance, fuel dispatch, and associated financial implications until the fiscal year 2029-30. Grounded in data and market intelligence, this study highlights the increasing role of renewable energy sources and emphasizes their significance in achieving a sustainable and resilient power sector. By exploring alternative scenarios, the analysis underscores the need to align with the Alternative and Renewable Energy (ARE) policy, bridging the gap between the 2030 renewable energy targets and actual installation. This paper aims to provide valuable insights to policymakers and stakeholders, enabling informed decision-making for a balanced, cost-effective, and environmentally friendly power system aligned with national energy goals.
The evaluation of socio-economic potential plays a pivotal role in advancing sustainable transportation systems, particularly in the field of the infrastructure for electric vehicle charging. In this study, we present a comprehensive methodology that integrates the Analytic Hierarchy Process (AHP) and machine learning techniques to evaluate the socio-economic potential of the Marrakech-Safi region. By employing AHP, we determine the target variable, and subsequently apply RF (Random Forest) and SVM (Support Vector Machine) models incorporating 11 key factors such as demographics, road network, public facilities, typology, and power grid. The findings of our study reveal that the RF model, with an accuracy of 96.37%, outperforms the SVM model, which achieved an accuracy of 94.81%, in accurately predicting the socio-economic potential of our region. Building upon these results, we employ the RF model to project the potential of the Casablanca-Settat region, uncovering promising opportunities for the construction of an infrastructure for electric vehicle charging, notably in the city of Casablanca. The information provided by this study hold significant implications for decision-makers and policymakers involved in the planning and promotion of sustainable transportation infrastructure. By leveraging the combination of AHP and machine learning techniques, our methodology provides a solid framework for evaluating a region's socioeconomic potential, contributing to the formulation of informed strategies for sustainable transportation systems.
Generally, the output voltage of a PWM inverter that drives SPMSM (Surface Permanent Magnet Synchronous Motor) has many harmonic components caused by switching. Harmonic components cause EMI (Electro Magnetic Interference), noise and leakage current problems. LPF (Low Pass Filter) including LC filter is effective to suppresses harmonics. LC filters have the potential for resonance phenomena. In addition, because of the presence of two dynamics, multivariable control based on modern control theory is required. Stable control is possible for SPMSM with Active LC filter, using ILQ (Inverse Linear Quadratic) control, which can realize robust multivariable control, and the IRM (Inverse Reference Model), which is two-degree-of-freedom control system. In this paper, Simulations confirm the effectiveness and robustness of the active LC filter. Controlling the SPMSM with active LC filter by this control method can suppress harmonics and contribute to the improvement of EMI problems and noise.
This paper focuses on investigating circulating currents arising from unequal power distribution within a parallel-connected inverter system. Circulating currents emerge when there are disparities in the output voltages of inverters linked in parallel. Unequal power sharing situations can also trigger these circulating currents. Multiple control strategies exist to attain balanced power sharing in parallel systems. This study achieves power sharing equilibrium through the implementation of a droop control approach. The experimental setup includes two parallel-connected inverters, modeled using MATLAB/Simulink. Circulating currents are simulated across different loading rates, ranging from 10% to 50%, between these inverters. The simulation results demonstrate that circulating currents are effectively eliminated when power is evenly shared among the inverters. Conversely, an increase in circulating current is observed with an increasing imbalance in power distribution ratios among the inverters.
Solar energy is a valuable and sustainable source of power. Researchers are exploring various methods to optimize its utilization, including solar tracking systems. These systems aim to increase power generation by aligning solar panels with the sun's position. Traditional solar tracking approaches have shown 30-40% improvements compared to static panels. However, loT (Internet of Things) technology advancements have opened doors for intelligent solar tracking systems with increased functionality compared to sun-sensing systems. This study presents an innovative, smart solar tracking system that leverages loT technologies. By utilizing low-cost, compact computers like Raspberry Pi Zero 2 W, the system gathers real-time data from the internet to accurately track the sun's movement. Additionally, loT capabilities allow the system to access weather information, enhancing the possibilities for panel protection. The proposed loT solar tracking system offers a comprehensive and efficient solution. Measurements and comparative analyses are conducted to evaluate the performance of the solar power efficiency system using the loT solar tracking system, comparing it to traditional static systems and sensor- based tracking systems. This research aims to contribute to solar energy optimization and highlight the benefits of integrating loT technologies into solar tracking systems. The findings of this study will demonstrate the potential for maximizing power output and fostering sustainable energy solutions. Adopting loT solar tracking systems can increase solar energy utilization, accelerating the transition toward a cleaner and more efficient future.
The advancements in aircraft technology, particularly the increased electrification of aircraft, have introduced complexities in designing and integrating electrical systems. Hardware-in-the-loop (HIL) simulation is recognized as a crucial tool in the aerospace industry, utilizing Field-Programmable Gate Arrays (FPGAs) for their high-speed data processing and low latency. This paper presents a method for accurate real-time simulation of cable harnesses in modern aircrafts, employing Frequency-Dependent Network Equivalent (FDNE) models and FPGAs. The implementation methodology and results of the FPGA-based HIL simulation are presented and discussed. Various equivalent models with different data types are evaluated to determine achievable time-step and resource consumption. Results show the effectiveness of the proposed method in achieving sub-microsecond time-step.
Wireless sensor nodes (WSNs) are typically powered by batteries, which results in a maintenance burden and cost, especially in Internet of Things (IoT) scenarios that install many devices. For these applications, the ideal node must be set-and-forget, maintenance-free, and low-cost. This paper proposes a self-powered wireless sensor platform, implemented through a photovoltaic cell and with LoRa connectivity. An off-the-shelf amorphous silicon photovoltaic cell of 58.1 mm $\times 48.6$ mm is used to harvest and sense indoor ambient light with a limit of detection of 200 lux. The system embeds a 2 mF storage capacitor that every cycle stores enough energy to send a maximum of 30 bytes data packet when the LoRa transmitter is configured with a spreading factor of 7, a bandwidth of 125 KHz, and an output transmitted power of 14 dBm. Experimental measurements performed in an urban area have shown that a 20 bytes data packet is received correctly and reliably at the maximum distance of 560 m.
Current measurement errors in permanent magnet synchronous motor (PMSM) drives can lead to undesired torque and speed ripples, significantly impairing control performance. To address this issue, this paper presents a novel online current error correction method for PMSM drives. The proposed approach takes into account both current scaling errors and offset errors, aiming to achieve precise error correction without relying on signal injection or prior knowledge of machine parameters. First, the relation between the different types of current errors and the machine speed harmonic is derived, laying the foundation for current measurement correction. Then, the speed harmonic is explored to search for actual current errors with the gradient descent algorithm. By minimizing the specific order of speed harmonics, the proposed algorithm achieves precise correction for current measurement errors without signal injection or knowledge of machine parameters. Through extensive simulation studies, the efficacy of the proposed current error correction algorithm is thoroughly validated. The results demonstrate significant improvements in control performance, confirming the method's ability to mitigate both current offset and scaling errors in a PMSM drive.
As part of the energy transition, controlling energy consumption is a challenge for everyone. To this end, a number of sustainable solutions are being proposed, notably for BIPV (Building Integrated Photovoltaics) buildings. In addition, artificial intelligence (AI) is an effective tool for analyzing photovoltaic (PV) energy production and consumption data. It will then be possible to predict the PV energy production of a BIPV building or any other system integrating PV panels. This paper presents the implementation of artificial learning models for the prediction of the very short-term energy production of PV panels in a Positive Energy Winter House (PEWH). These are methods based on multivariate time series, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and a hybrid model. In the case of a winter house with a long period of snow, accurate prediction of solar power output in the very short term is needed to face the fluctuation that can be caused by climate. In this study, we are working on a winter house located in Poschiavo. The proposed method is applied to data recorded in real time by PEWH's photovoltaic solar panels, and the results are compared and tested over period of time. The results confirm the validity of each proposed model in forecasting PV Energy.
Li-ion batteries are widely used in electric vehicles (EVs), but they suffer from battery degradation, particularly in terms of calendar loss. Machine learning has been used to predict battery health deterioration due to cyclic loss, but the accuracy depends on input feature selection. This study introduces an improved feature selection method, enhancing battery calendar loss prediction. Eight machine learning algorithms are applied to an EV battery dataset, and results show improved prediction accuracy and reduced mean absolute error (MAE). Notably, Gaussian process regression (GPR), random forest (RF) regression, and XGBoost methods combined with the proposed feature selection method show the most significant accuracy improvement.
The current decade will see the rise of electric mobility, particularly in the field of freight transportation. This is happening for a variety of reasons, some of which are inexpensive transportation for everybody, reduced emissions of greenhouse gases, and rising crude oil prices, amongst others. In this light, it is crucial to investigate the impact of the socio-political arena, which is and always has been one of the most important variables upon which the success of future electric freight mobility depends. To learn more about the societal and political variables at play, we have conducted a user-focused interview. The positive findings from this study will aid planners at both the federal and state levels, as well as those at original equipment manufacturers.
Energy prices are rising due to the changing global situation. Rising energy prices have also led to higher electricity prices in Japan. Electricity prices is determined by the contracted power for the past year. Hospitals and clinics would like to reduce electricity costs with decreasing contracted power. They recently have a combination of diesel generators (DGs) and PV for their power systems. DGs and PV can be used for peak-cut. Therefore, DGs and PV has an important role for peak-cut operations. This paper proposes a forecasting method of peak-cut of power demand using LSTM at a clinic. Four cases based on the correlation of the demand data are defined as input data for LSTM in this paper. The results show that Case 3 and Case 4 is a better model on the point of forecasting peak demand. Power demand data for last three months produced better results than using five years of power demand data.
This paper proposes an optimum switching patterns of a matrix converter for reducing switching loss under various input power factors and load power factors. Matrix converter has 27 switching states and several switching states are chosen in order to control an output voltage and an input current at the same time. When the matrix converter selects 4 switching states, there are 1278 switching patterns totally. By using these 1278 patterns, the matrix converter can reduce an input current distortion or an output voltage distortion. In addition, the matrix converter can reduce switching loss. However, the control scheme of the matrix converter reducing switching loss. This paper discusses optimized switching patterns for reducing an output voltage distortion, an input current distortion, or switching loss when a modulation index, input power factor and load power factor are changed. The output voltage and input current distortion are evaluated by an instantaneous effective values theory. In addition, this paper introduces a switching loss coefficient in order to reduce the switching loss. By using this coefficient, this paper demonstrates minimization results of the switching loss by theoretical simulation.
