The fabrication of Metal-DNA-Metal (MDM) structure-based high sensitivity sensors from DNA micro-and nanoarray strands is a key issue in their development. The tunable semiconducting response of DNA in the presence of external electromagnetic and thermal fields is a gift for molecular electronics. The impact of temperatures (25-55 °C) and magnetic fields (0-1200 mT) on the current-voltage (I-V) features of Au-DNA-Au (GDG) structures with an optimum gap of 10 μm is reported. The I-V characteristics acquired in the presence and absence of magnetic fields demonstrated the semiconducting diode nature of DNA in GDG structures with high temperature sensitivity. The saturation current in the absence of magnetic field was found to increase sharply with the increase of temperature up to 45 °C and decrease rapidly thereafter. This increase was attributed to the temperature-assisted conversion of double bonds into single bond in DNA structures. Furthermore, the potential barrier height and Richardson constant for all the structures increased steadily with the increase of external magnetic field irrespective of temperature variations. Our observation on magnetic field and temperature sensitivity of I-V response in GDG sandwiches may contribute towards the development of DNA-based magnetic sensors.
The reactive power control of a power system is discussed under two types of variables: continuous variables (e.g., generator bus voltages) and discrete variables (e.g., transformer taps and the size of switched shunt capacitors). This paper proposes a novel and powerful algorithm, named turbulent flow of water-based optimization (TFWO) as well as a new improved version of this algorithm, called θ-TFWO, for optimal reactive power distribution (ORPD) to reduce losses. The proposed method is applied to two large-scale IEEE 57-bus systems. Furthermore, to demonstrate the competitive performance of the suggested algorithm, its performance was compared to that of several other algorithms, including biogeography-based optimization (BBO), social spider algorithm (SSA), and optics inspired optimization (OIO), in terms of solving the ORPD problem. The results confirmed the robustness and effectiveness of the proposed method as a powerful optimizer applicable to optimal reactive power distribution in power systems.
Mineral oil has been chosen as an insulating liquid in power transformers due to its superior characteristics, such as being an effective insulation medium and a great cooling agent. Meanwhile, the performance of mineral oil as an insulation liquid can be further enhanced by dispersing nanoparticles into the mineral oil, and this composition is called nanofluids. However, the incorporation of nanoparticles into the mineral oil conventionally causes the nanoparticles to agglomerate and settle as sediment in the base fluid, thereby limiting the improvement of the insulation properties. In addition, limited studies have been reported for the transformer oil as a base fluid using Aluminum Oxide (Al2O3) as nanoparticles. Hence, this paper reported an experimental study to investigate the significant role of cold plasma treatment in modifying and treating the surface of nano-alumina to obtain a better interaction between the nano-alumina and the base fluid, consequently improving the insulation characteristics such as breakdown voltage, partial discharge characteristics, thermal conductivity, and viscosity of the nanofluids. The plasma treatment process was conducted on the surface of nano-alumina under atmospheric pressure plasma by using the dielectric barrier discharge concept. The breakdown strength and partial discharge characteristics of the nanofluids were measured according to IEC 60156 and IEC 60270 standards, respectively. In contrast, the viscosity and thermal conductivity of the nanofluids were determined using Brookfield DV-II + Pro Automated viscometer and Decagon KD2-Pro conductivity meter, respectively. The results indicate that the 0.1 wt% of plasma-treated alumina nanofluids has shown the most comprehensive improvements in electrical properties, dispersion stability, and thermal properties. Therefore, the plasma treatment has improved the nanoparticles dispersion and stability in nanofluids by providing stronger interactions between the mineral oil and the nanoparticles.
Coronavirus 2019 (COVID-19) has globally affected the human mortality rate and economic history of the modern world. According to the World Health Organization, COVID-19 has caused a severe threat to the health of the vulnerable groups, notably the elderly. There is still some disagreements regarding the source of the virus and its intermediate host. However, the spread of this disease has caused most countries to enforce strict curfew laws and close most industrial and recreational centres. This study aims to show the potential positive effects of COVID-19 on the environment and the increase of renewable energy generation in Malaysia. To prevent the spread of this disease, Malaysia enacted the Movement Control Order (MCO) law in March 2020. Implementation of this law led to a reduction in environmental pollution, especially air pollution, in this country. The greenhouse gases (GHG) emission , which was 8 Mt CO2 eq. from January 2020 to March 2020, reduced to <1 Mt CO2 eq. for April and May. The reduction of GHG emission and pollutant gases allowed more sunlight to reach photovoltaic panels, hence increasing the renewable energy generation.
In this paper, the optimal allocation of constant and switchable capacitors is presented simultaneously in two operation modes, grid-connected and islanded, for a microgrid. Different load levels are considered by employing non-dispatchable distributed generations. The objective function includes minimising the energy losses cost, the cost of peak power losses, and the cost of the capacitor. The optimization problem is solved using the spotted hyena optimizer (SHO) algorithm to determine the optimal size and location of capacitors, considering different loading levels and the two operation modes. In this study, a three-level load and various types of loads, including constant power, constant current, and constant impedance are considered. The proposed method is implemented on a 24-bus radial distribution network. To evaluate the performance of the SHO, the results are compared with GWO and the genetic algorithm (GA). The simulation results demonstrate the superior performance of the SHO in reducing the cost of losses and improving the voltage profile during injection and non-injection of reactive power by distributed generations in two operation modes. The total cost and net saving values for DGs only with the capability of active power injection is achieved 105,780 $ and 100,560.54 $, respectively and for DGs with the capability of active and reactive power injection is obtained 89,568 $ and 76,850.46 $, respectively using the SHO. The proposed method has achieved more annual net savings due to the lower cost of losses than other optimization methods.
The static synchronous series compensator (SSSC) can add a series reactance to the transmission line, and when it is fed using auxiliary signals, it can participate in damping inter-area oscillations by changing the series reactance. In this paper, the effect of the SSSC on small-signal stability is investigated. The design of a controller for damping oscillations is designed and discussed. Moreover, using the firefly and the harmony search algorithms, the optimal parameters controlling SSSC are addressed. The effectiveness of these two algorithms and the rate of SSSC participation in damping inter-area oscillation are also discussed. MATLAB software was used to analyse the models and to perform simulations in the time domain. The simulation results on the sample system, in two areas, indicated the optimal accuracy and precision of the proposed controller.
This paper proposes an improved hierarchical control strategy consists of a primary and a secondary layer for a three-phase 4-wire microgrid under unbalanced and nonlinear load conditions. The primary layer is comprised of a multi-loop control strategy to provide balanced output voltages, a harmonic compensator to reduce the total harmonic distortion (THD), and a droop-based scheme to achieve an accurate power sharing. At the secondary control layer, a reactive power compensator and a frequency restoration loop are designed to improve the accuracy of reactive power sharing and to restore the frequency deviation, respectively. Simulation studies and practical performance are carried out using the DIgSILENT Power Factory software and laboratory testing, to verify the effectiveness of the control strategy in both islanded and grid-connected mode. Zero reactive power sharing error and zero frequency steady-state error have given this control strategy an edge over the conventional control scheme. Furthermore, the proposed scheme presented outstanding voltage control performance, such as fast transient response and low voltage THD. The superiority of the proposed control strategy over the conventional filter-based control scheme is confirmed by the 2 line cycles decrease in the transient response. Additionally, the voltage THDs in islanded mode are reduced from above 5.1% to lower than 2.7% with the proposed control strategy under nonlinear load conditions. The current THD is also reduced from above 21% to lower than 2.4% in the connection point of the microgrid with the offered control scheme in the grid-connected mode.
In this paper, the technical-economic framework for designing of water pumping system based on photovoltaic clean energy with water tank storage is presented to supply drinking water of customers for remote areas. The objective function is to minimize the net present cost (NPC) (as economic index) including initial investment costs, maintenance, and replacement costs, and reliability constraint is defined as customer's water not supplied probability (CWNSP) as technical index. A meta-heuristic intelligent water drops algorithm (IWDA) is proposed to optimize the solar water pumping system considering NPC and CWNSP with high accuracy and speed of optimization in achieving the global solution. The simulation results show that the proposed method is capable of responding to customer's water demand by optimally sizing components and water storage tank based on IWDA which is inspired based on flowing the water drops in rivers by achieving the lowest cost with optimal reliability. The NPC of the system with CWNSP equal to 3.17 % is obtained 0.24 M$ for 6-m-high water extraction. The results showed that with increasing the water extraction height, the NPC increased, and the reliability also weakened. Moreover, the superiority of the IWDA is confirmed compared with particle swarm optimization (PSO) in designing a water pumping system with the lowest NPC.
Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.
Photovoltaic (PV) system is the cleanest form of electricity generation, and it is the only form with no effect on the environment at all. However, some environmental challenges persist, which must be overcome before solar energy may be used to represent a source of truly clean energy. This paper aims to study the stability and dynamic behavior of a grid-connected environmentally friendly photovoltaic energy system using the bifurcation theory. This theory introduces a systematic method for stability analysis of dynamic systems, under changes in the system parameters. To produce bifurcation diagrams based on the bifurcation theory, a parameter is constantly changed in each step, using MATLAB and AUTO, and eigenvalues are monitored simultaneously. Considering how the eigenvalues approach the system's imaginary axis in accordance with the changes in the targeted parameter, the occurred saddle-node and Hopf bifurcations of the grid-connected PV system are extracted. Using the obtained bifurcations, the system's dynamic stability limits against changes in controlled (controller coefficients) and systematic parameters (such as the Thevenin impedance network) are found.
Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data.
A circular economy promotes a world-friendly style of economic development, and the main aim is a closed-loop cycle of "resource-production-consumption-regeneration" economic activities. A circular economy can assist in implementing sustainable development in the food industry. During conventional food processing, the thermal effects degrade the food residues and make their wastes. Nonthermal processing has emerged as a promising, safe, and effective technique for extracting bioactive compounds from food residues. Nonthermal processing help in implementing a circular economy and meeting the United Nations approved Sustainable Development Goals (SDGs). Collaboration between food producers and the food chain may make the food industry more circular. This article motivates adopting nonthermal technologies to conserve natural resources, food safety, and energy in different food processing phases to meet SDGs.