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Conversion efficiency, power production, and cost of PV panels’ energy are remarkably impacted by external factors including temperature, wind, humidity, dust aggregation, and induction characteristics of the PV system such as tilt angle, altitude, and orientation. One of the prominent elements affecting PV panel performance and capability is dust. Nonetheless, dust features including size, shape, type, etc. are geologically known. Several mitigation methods have been studied for the reduction of dust concentration on the exterior face of the PV modules. The outcomes have demonstrated that dust concentration and pollutants remarkably affect the PV panel energy production. This paper reviews the recently developed research on the outcomes of the dust effect on PV panels in different locations and meets the needs of future research on this subject. Moreover, different cleaning methods that could be advantageous for future researchers in opting for the most applicable technique for dust removal are reviewed.

Power quality issues can affect the performance of devices powered by the grid and can, in severe cases, permanently damage connected devices. Events that affect power quality include sags, swells, waveform distortions and transients. Transients are one of the most common power quality disturbances and are caused by lightning strikes or switching activities among power-grid-connected systems and devices. Transients can reach very high magnitudes, and their duration spans from nanoseconds to milliseconds. This study proposed a deep-learning-based technique that was supported by convolutional neural networks and a bidirectional long short-term memory approach in order to detect and characterize power-quality transients. The method was validated (i.e., benchmarked) using an alternative algorithm that had been previously validated according to a digital high-pass filter and a morphological closing operation. The training and performance assessments were carried out using actual power-grid-measured data and events.

Hybrid PVT systems simultaneously produce electrical energy using photovoltaic technology and thermal energy using a heat extraction method that collects induced heat from the module. The purpose of this work is to establish a PVT system based on characterization, efficiency study, and performance analysis for both an electrical and a thermal system. A mathematical analysis of the electrical, thermal, and optical model is performed to establish the proposed system. Three types of heat exchanger pipes, including stainless steel, aluminum, and copper, are considered for a heat transfer analysis of the system. The results include temperature profiling, a comparison of the PVT system’s different components, and an overall output and efficiency study for all of the mentioned pipes. Results show that the obtained electrical and thermal efficiency for stainless steel is 0.1653 and 0.237, respectively, for aluminum it is 0.16515 and 0.2401, respectively, and for copper it is 0.16564 and 0.24679, respectively. After comparison, it was found that the overall efficiency for stainless steel is 0.40234, for aluminum is 0.40526, and for copper is 0.41244. Thus, this study will enhance the opportunity to provide an effective hybrid PVT energy management system.

Hybrid PVT solar systems offer an innovative approach that allows solar energy to be used to simultaneously generate thermal and electrical energy. It is still a challenge to develop an energy-efficient hybrid PVT system. The aim of this work is to develop a mathematical model, investigate the system’s performance based on parameters, include sensitivity analysis in the upper layer mainly photovoltaic part, and provide an efficient and innovative system. Performance analysis of the hybrid system is obtained by establishing a mathematical model and efficiency analysis. The electrical model and thermal model of the hybrid system is also obtained by appropriate and complete mathematical modeling. It establishes a good connection of the system in the context of electrical analysis and power generation. The parameters variation impact and sensitivity analysis of the most important parameters, namely, irradiance, ambient temperature, panel temperature, wind speed, and humidity in the PV panel section, are also obtained using a MATLAB model. The results show the effective increase or decrease in the electrical power and sensitiveness in the output of the system due to this modification. Related MPP values as a result of these parameters variation and their impact on the overall output of the hybrid PVT system are also analyzed.

Recently, the estimation of photovoltaic parameters has drawn the attention of researchers, and most of them propose new optimization methods to solve this problem. However, the process of photovoltaic parameters estimation can be affected by other aspects. In a real experimental setup, the I–V characteristic is obtained with IV tracers. Depending on their technical specifications, these instruments can influence the quality of the I–V characteristic, which in turn is inevitably linked to the estimation of photovoltaic parameters. Besides the uncertainties that accompany the measurement process, a major effect on parameters estimation is the size of the measurement interval of current and voltage, where some instruments are limited to measure a small portion of the characteristic or cannot reach their extremum regions. In this paper, three case studies are presented to analyse this phenomenon: different characteristic measurement starting points and different measurement intervals. In the simulation study the parameters are extracted from 1000 trial runs of the simulated I-V curve. The results are then validated using an experimental study where an IV tracer was built to measure the I–V characteristic. Both simulation and experimental studies concluded that starting the measurements at the open circuit voltage and having an interval spanning a minimum of half of the I–V curve results in an optimal estimation of photovoltaic parameters.

International standards from IEC and IEEE regulate power grid parameters such as theRMSvalue, frequency, harmonic and interharmonic distortion, unbalance or the presence of transients, that are important to assure the quality of distributed power. Standard IEC 61000-4-30 suggests the zero crossing algorithm for the measurement of the power grid frequency, but also states that different algorithms can be used. This paper proposes a new algorithm, the Fractional Interpolated Discrete Fourier Transform, FracIpDFT, to estimate the power grid frequency, suitable for implementation in resource limited embedded measurement systems. It is based on the non-integer Goertzel algorithm followed by interpolation at non-integer multiples of the DFT frequency resolution. The proposed algorithm is validated and its performance compared with other algorithms through numerical simulations. Implementation details of the FracIpDFT in an ARM Cortex M4 processor are presented along with frequency measurement results performed with the proposed algorithm in the developed system.

The degradation of photovoltaic modules and their subsequent loss of performance has a serious impact on the total energy generation potential. The lack of real-time information on the output power leads to additional losses since the panels may not be operating at their optimal point. To understand the behaviour, numerically simulate the characteristics and identify the optimal operating point of a photovoltaic cell, the parameters of an equivalent electrical circuit must first be identified. The aim of this work is to develop a total least-squares based algorithm which can identify those parameters from the output voltage and current measurements, taking into consideration the uncertainties on both measured quantities. This work presents a comparative study of the Ordinary Least Squares (OLS) and Total Least Squares (TLS) approaches to the estimation of the parameters of a photovoltaic cell.

This work describes the development of an embedded standalone measurement system that monitors the aging of batteries using impedance spectroscopy. The system generates a multisine stimulus that contains the frequency components at which the battery impedance is measured. Coherent generation and sampling is assured, and Goertzel filters, one for each measurement frequency, are updated with each new sample. This architecture reduces memory requirements because the current and voltage of the measured samples are discarded after processing. Aging is monitored, as the system is able to automatically perform complete or partial charge/discharge cycles as well as measurement cycles without requiring user interaction.

The evolution of rechargeable battery characteristics have led to their use in almost every device in our everyday life. This importance has also increased the relevance of estimating the remaining battery charge (state of charge, SOC) and their health (state of health, SOH). One of the methods for the estimation of these parameters is based on the impedance spectroscopy obtained from the battery output impedance measured at multiple frequencies. This paper proposes an embedded measurement system capable of measuring the battery output impedance while in operation (either charging or supplying power to the intended device). The developed system generates a small amplitude stimulus that is added to the battery current. The system then measures the battery voltage and current to estimate the impedance at the stimulus frequencies. Three batteries were measured at different SOC levels, demonstrating the system principle of operation. Complementarily, a battery impedance equivalent circuit was used, together with genetic algorithms, to estimate the circuit parameters and assess their dependence on the battery SOC.

Porous baffles can be used to enhance heat transfer in various engineering applications, including electronic cooling, gas turbine blades, and chemical reactors. Also, the backward-facing step is a widely used configuration in fluid dynamics studies due to its simplicity and relevance to real-world geometries. This study examines heat transfer and flow characteristics in a backward-facing step channel featuring a heated bottom wall and two porous baffles. A computational fluid dynamics model, validated against prior research, is used to investigate flow and temperature fields. The innovation of this work lies in the application of multi-objective optimisation to search for a set of solutions that establish a trade-off between the average Nusselt number and the pressure drop. The optimisation specifically considers various parameters of the porous baffles, including height, width, distance from the step, and Darcy number, to identify optimal design configurations. Results show that porous baffles significantly improve heat transfer compared to a backward-facing step channel without them, despite an increase in pressure drop due to their presence. This work offers valuable insights into the trade-off between heat transfer performance and pressure drop, crucial for designing efficient heat transfer systems. By exploring the Pareto-Frontier, which represents various optimal design solutions, the study provides practical guidance when seeking to optimise heat transfer in backward-facing step channels with porous baffles. The findings contribute to advancing the understanding of heat transfer enhancement, highlighting the potential of porous baffles as a viable solution for improving thermal management in engineering systems.