This paper proposes an updated two-step approach to improve the operation of a distribution network (DN) through the optimal siting and sizing of one, two, or three systems, each consisting of a photovoltaic (PV) generator and a battery energy storage (BES) unit – the so-called “PV-BES system”. The first step of the approach determines the optimal sites and optimal powers of the PV-BES systems, taking into account the DN load profile and the required improvement in DN operation. Parameters used to describe the quality of the DN operation include the average daily power losses and the voltage profile quality index of the DN. The optimal locations and optimal powers of the PV-BES systems are determined using metaheuristics of the particle swarm optimisation (PSO) and wild horse optimisation (WHO) methods considering various daily load profiles. Based on the optimal powers of the first step and the known daily variation of solar irradiation, the second step provides the individual maximum powers of the PV generators and BES units, as well as the storage capacities of the BES units required for the sizing of the PV-BES systems. The iterative method used for the sizing of the PV-BES systems in the second step of the proposed approach can be regarded as a novelty. Finally, all results were obtained using the IEEE 33-bus test radial DN topology, considering different numbers of PV-BES systems connected to the DN, different efficiencies of the BES units, and different priorities in the criterion function.

An acoustic emission and machine learning based pistachio classification system has been developed. This system performs feature extraction using Mel frequency cepstral coefficients (MFCC) and classification using support vector machine (SVM). This study revealed that when closed-shelled pistachios hit a steel plate, they have different frequency components compared to open-shelled pistachios. The audio signals of the samples selected for the classification process were recorded using a high sensitivity carbon microphone and MATLAB Analog Input Recorder. These recorded sounds were processed by applying a hamming window to remove ambient noise and make them more clearly analyzable. MFCC is one of the leading methods used to extract features representing audio signals. In this study, MFCCs are used to distinguish between open and closed shelled pistachios. By analyzing the frequency components in audio signals, this feature extraction method helps to identify the distinctive features of the signals. These features are given as input to a support vector machine algorithm called FITCSVM for classification. FITCSVM is an algorithm that can perform one-class and two-class (binary) classification on low or medium-sized prediction datasets. In this study, open and closed shelled pistachios were classified with high accuracy. The results show that the acoustic emission and machine learning based classification system has the potential to be used in the pistachio industry. In particular, distinguishing between open and closed shelled pistachios is of great importance for increasing product quality and improving processing processes. As a result, this research shows that MFCC and SVM algorithms can be used effectively in pistachio classification. The sound signals obtained by acoustic emission method were analyzed with MFCC to extract the features required for classification and FITCSVM was used to classify with high accuracy. Such innovative approaches can contribute to the development of more efficient and effective methods for processing agricultural products.

This paper presents high-frequency universal filter applications based on a voltage differential buffered amplifier (VDBA) using 32 nm fin field effect transistor (FinFET) technology. FinFET technology is a promising alternative to complementary metal-oxide-semiconductor (CMOS) technology to avoid the problems caused by the decrease in transistor size as the technology evolves. In addition to the manufacturing process being similar to CMOS technology, FinFET technology offers many advantages, such as reduced short channel effects, higher drain current, reduced static leakage current, faster switching time, lower supply voltage, lower power consumption, and higher efficiency. The VDBA active circuit block, which has high input impedance and low output impedance, is preferred for high-frequency and high-bandwidth applications. It is advantageous to design active filter circuits using VDBA because of its superior features, such as lower power consumption, higher bandwidth, wider range linearity, and the ability to implement the proposed circuits without external resistors. In this study, FinFET-based VDBA and filter application are simulated with the Spice simulation programme using 32 nm PTM technology parameters. Simulation results using 32 nm FinFET technology are compared with those using 0.18 µm TSMC technology. It is concluded that 32 nm FinFET technology reduces power consumption by 98.8 % and increases bandwidth by 145 times. The successful results show that FinFET technology is superior to CMOS technology in analogue circuit design. FinFET-based VDBA circuits and filters will be more advantageous in the design of signal processing and biomedical applications.

Managing energy in batteryless Internet of Things (IoT) nodes, especially in solar-powered mesh networks, presents significant challenges. This paper introduces an advanced solar irradiance model that simulates detailed daily energy profiles. The model considers various azimuth and elevation angles of solar panels, as well as cloud cover. Accurate simulation of daily energy production is critical for optimising the behaviour of solar-powered IoT nodes. The results highlight the utility of the model as a robust tool for research and simulations involving batteryless IoT devices, emphasising its enhanced capabilities for precise energy management and optimisation. This study offers a reliable framework for predicting and managing energy production in solar-powered IoT networks, thus supporting the development of more efficient and sustainable IoT systems.

To improve the operational efficiency and reliability of photovoltaic power stations, this paper introduces a novel approach to detect outliers in photovoltaic arrays using a Vine-Copula method. The procedure is divided into two distinct phases. Initially, it identifies deviations in the direct current (DC) component of the photovoltaic (PV) system. The following phase extends this by pinpointing irregularities in the DC voltage of the array. To model the interconnection between the PV current, irradiance, and temperature, the Vine-Copula is employed in this process. The optimisation of this function is based on the Akaike information criterion. Subsequently, a conditional probability model for the PV current is developed along with a formula to determine the quantile of this probability. This interval is then employed as the primary metric for detecting and eliminating current deviations. After refining the current data, a similar approach is taken to address voltage irregularities. The results of the simulation tests indicate that this proposed method is more effective, showing lower error rates and higher accuracy in detecting outliers, compared to other methods.

As the penetration rate of renewable energy in new power systems continues to increase, these systems face serious frequency control issues. The limitations of traditional methods for addressing frequency control lie primarily in their reliance on the frequency regulation capability of a single battery energy storage system (BESS). This dependence not only requires a complex communication infrastructure to transmit remote control signals but also is susceptible to communication delays, leading to system instability. This paper proposes a distributed BESS robust frequency control method (load frequency control (LFC)) based on a sparse communication network, aiming to address the limitations of traditional methods in terms of communication infrastructure requirements and the impact of communication delays. Subsequently, a dual-layer model predictive control (MPC) strategy is designed. The first layer uses a nominal model for predictive control, while the second layer considers system uncertainties for auxiliary control to improve the response characteristics of the BESS, thus significantly enhancing LFC performance and achieving more effective frequency regulation. Finally, simulation results show that under different parameter conditions, such as capacity, state of charge (SoC), and time constants, the response capability and frequency regulation effect of the distributed BESS are significantly better than those of traditional methods.

The demand for high-gain, efficient, and cost-effective power converters with simple control mechanisms to connect electric vehicle batteries to the grid is increasing. This study introduces a switched capacitor-based power boost converter circuit to meet these needs. The minimal number of power switches in the circuit simplifies control operations and improves practical applicability. The proposed converter boosts the battery pack voltage by a factor of 5 and 11 for duty ratios of 0.5 and 0.8, respectively, which is significant compared to conventional boost converters. However, designing an electromagnetic interference (EMI) filter is crucial when a power converter board requires the application of a wide range of switching frequencies to enhance electromagnetic compatibility (EMC) immunity. Therefore, as the next step, the EMI/EMC filter design stages were discussed for the proposed converter. For this purpose, 15 printed circuit board (PCB) design rules were checked using the Altium Designer EMI Design Rule Checker, and board EMI/EMC compatibility was analysed. The resonance between the power and ground layers in the PCB was assessed using the plane resonance analyser. The results of simulation and laboratory tests are presented, which confirms the theoretical studies. On the basis of the software results, the points on the electronic board most susceptible to visual interferences have been identified. To minimise these EMI/EMC errors, it is suggested to add electronic components, such as capacitors, at these points according to mathematical and software findings.

This paper focusses on using neural network models to predict the age of social media users based on their voice recordings. The objective is to identify potential risky interactions between minors and adults by comparing the declared and predicted age groups of the users. The paper addresses the selection and training of suitable models and evaluates their effectiveness in age prediction. The results are demonstrated in sample data, where performance metrics are analysed, and possible limitations of the method are identified. Finally, the implications of the results for the safety of minors on social networks are discussed, and suggestions for future research in this area are provided.

This paper proposes a design for a Dolph-Tschebyscheff-weighted microstrip antenna array using a deep learning application. For this purpose, a multilayer perceptron and a deep learning model, both created using the same data set generated by a genetic algorithm, were compared. The antenna array population is initially generated randomly and then optimised with a genetic algorithm. The data produced by this model becomes a data set used for training in the deep learning application. The dimensions and specifications of the antenna array are obtained from this application, ensuring precision and optimisation in the design process. A new microstrip antenna array structure is employed for the proposed method, taking advantage of this design technique. The Dolph-Tschebyscheff weights are applied to achieve better characteristics for the microstrip antenna array, thus obtaining low side lobe levels, which are crucial for enhancing signal clarity and reducing interference. The results demonstrate that the proposed algorithm significantly improves the specifications of the structure. This improvement highlights the potential for integrating deep learning with traditional optimisation algorithms for advanced antenna design.

Airborne wind energy (AWE) technology has emerged as a promising alternative to conventional wind turbines, harnessing stronger and more consistent winds at higher altitudes. This paper explores the potential of AWE systems in Turkey through a case study of the Hatay region. The study begins with the selection of the optimal two-parameter Weibull distribution model and compares various parameter estimation methods to accurately estimate wind speeds using wind speed data. This analysis is followed by a life cycle assessment (LCA) to quantify the global warming potential (GWP) and cumulative energy demand (CED) associated with the deployment of an AWE plant in Turkey. Additionally, a techno-economic assessment evaluates the economic viability of AWE systems over their operational lifetime through detailed cost modelling. Experimental verifications and comparisons with existing renewable energy technologies are also presented to validate the findings. The results demonstrate that AWE systems offer significant environmental and economic benefits, providing critical insights for policymakers, investors, and stakeholders. This study not only contributes to the growing body of AWE research, but also offers a replicable methodological framework for assessing AWE potential in other regions with similar wind energy prospects.