Elektronika ir Elektrotechnika

Risk Assessment Method for Distributed Power Distribution Networks Considering Network Dynamic Reconstruction

Tangyong Teng, Yu Huang, Juan Wang, Zhukun Li, Yonghua Chen — Mon, 26 Aug 2024 00:00:00 +0300

A new safety assessment framework has been proposed to address the operational risks of the integration of wind power and photovoltaic grid, which integrates the characteristics of distributed power sources with the dynamic reconfiguration requirements of the distribution grid. The framework comprehensively considers the impacts of wind power and photovoltaic output uncertainties, as well as load fluctuations, on the stability of the distribution grid. It also evaluates the safety under different operational states of the distribution grid. Using Halton sequence sampling technology to accurately simulate the output of distributed power sources and the status of system components, combined with CPLEX optimisation for solving, a dynamic reconfiguration model is constructed to address potential faults in the distribution grid. Introducing the combined weighting method, a comprehensive risk assessment system for voltage violations, power flow violations, and load shedding has been constructed. The effectiveness of this method has been validated through simulations on the IEEE33 bus and IEEE118 bus systems, providing new insights to improve the safety and reliability of distribution grids.

Airborne Wind Energy in Turkey with a Focus on Wind Resource Life Cycle Assessment and Techno-Economic Analysis

Ahmet Emre Onay, Emrah Dokur, Mehmet Kurban — Mon, 26 Aug 2024 00:00:00 +0300

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.

Title

Elektronika ir Elektrotechnika — Tue, 01 Oct 2024 00:00:00 +0300

A Deep Learning Application for Dolph-Tschebyscheff Antenna Array Optimisation

Mon, 26 Aug 2024 00:00:00 +0300

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.

Risk Assessment of Bird Collisions with a Wind Turbine Based on Flight Parameters

Mon, 26 Aug 2024 00:00:00 +0300

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

Logical Resonance in Izhikevich Neuron

Vedat Burak Yucedag, Ilker Dalkiran, Arash Ahmadi — Mon, 26 Aug 2024 00:00:00 +0300

This paper proposes a new logic element model based on an Izhikevich (IZ) neuron and neural system that emulates two- and three-state logic behaviours. In a noise-free environment, with a periodic current of suitable amplitude and frequency, the IZ system is capable of performing logical AND and OR operations. Initially, a single IZ neuron demonstrates membrane dynamics in response to an input signal generated by combining two-state logic currents below the threshold. Subsequently, an IZ neural system model is introduced to enhance the reliability and resilience of the system. This model is characterised by electrical coupling with fast conduction and chemical coupling with a more adaptable structure. Each logic input independently influences each neuron within the system. Additionally, it has been observed that the reliability of the logic element is influenced by changes in synaptic strength, with a neural system lacking sufficient synaptic strength failing to generate logical output. Furthermore, the system displays a three-state logic behaviour under suitable forcing periodicity, thus enhancing the power efficiency of the logic element. The proposed IZ neuron and neural system are expected to significantly impact the development of brain-inspired logic elements.

Relative Position Detection of Clustered Tomatoes Based on BlendMask-BiFPN

Caiping Guo, Can Tang, Yehong Liu, Xin Wang, Shumao Wang — Mon, 26 Aug 2024 00:00:00 +0300

In robotic harvesting, maneuvering around obstacles to position manipulators is challenging, especially in unstructured environments. This study proposes a method to detect the relative position of tomato bunches to the main stem position using the BlendMask-BiFPN algorithm. Initial comparative tests between full-stem and partial-stem labelling strategies revealed that the latter produced more complete peduncle masks, which guided our choice for subsequent experiments. Significant modifications to the BlendMask algorithm included the integration of a ResNet-101-BiFPN backbone, which improved the feature fusion network of the model. The revised model demonstrated high efficiency in pinpointing the relative positions of clustered tomatoes, achieving 91.3 % ARmask 50 and 84.8 % APmask 50 for the detection of tomato bunches. Comparisons with Mask RCNN, YOLACT, YOLACT++, and YOLOv8 showed that the BlendMask-BiFPN model outperforms these alternatives, suggesting its potential for more effective robotic harvesting in complex agricultural scenarios.

Concept of Speaker Age Estimation Using Neural Networks to Reduce Child Grooming

Renat Haluska, Monika Badovska, Matus Pleva — Mon, 26 Aug 2024 00:00:00 +0300

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.

A New Metaheuristic Approach to Diagnosis of Parkinson’s Disease Through Audio Signals

Ozer Oguz, Hasan Badem — Mon, 26 Aug 2024 00:00:00 +0300

Parkinson’s disease is accepted as one of the most important diseases in the world. Parkinson’s disease can be diagnosed in various conventional techniques. Recently, these techniques have been replaced by artificial intelligence systems. This study proposes a feature selection and classification technique for Parkinson’s disease based on speech signals using a meta-heuristic algorithm. The proposed method selects the features from the data set including speech signal data that most accurately represent the problem using the efficient search strategies of the immune plasma algorithm (IPA). The experimental results are promising compared to other competing methods for diagnosing Parkinson’s disease in the literature.

Editorial Board

Elektronika ir Elektrotechnika — Tue, 01 Oct 2024 00:00:00 +0300

Robustness Stability Analysis of Higher-Order DPCM Prediction Filters

Mon, 26 Aug 2024 00:00:00 +0300

This paper considers the robustness of the differential pulse-code modulation system with higher-order predictors. Special attention is paid to the robust parametric stability of the prediction filters with respect to the predictor coefficients. A generalisation of robustness in the classical sense is performed, and appropriate relations for calculating the probability of robustness are derived using Kharitonov principle. The proposed robustness estimation method is used for the third- and fourth-order prediction filters on speech signals, where the application of traditional methods is too difficult. For this reason, the Monte Carlo method is used to solve complex probability integrals. Verification and error analysis are performed for the previously considered second-order predictor. Satisfactory predetermined accuracy is achieved by increasing the number of samples. The results obtained could be very useful to design a system with suitable values for the predictor coefficients.