Reliability Of Energy Systems Research Articles

DEEP RECURRENT NEURAL NETWORKS IN ENERGY DEMAND FORECASTING: A CASE STUDY OF KAZAKHSTAN'S ELECTRICAL CONSUMPTION

The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan's electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships. The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN's exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN's promise as a robust forecasting tool, pivotal for the energy sector's future resilience and efficiency.

«Зелена» енергетика як основа економічного відновлення України в умовах нестабільності зовнішнього середовища

Green energy has become especially important in the modern world due to the exacerbation of climate change problems, the depletion of traditional energy sources and the need to maintain environmental stability. In the context of changes in the natural and socioeconomic environment, the transition to renewable energy is an important part of the development strategies of different countries. Ukraine, which is currently facing economic and political challenges, has a huge potential for the development of green energy, which can become a key factor in Ukraine’s economic recovery. The aim of the article is to study and analyze the conception of green energy as the basis for the economic recovery of Ukraine in the context of instability of the external environment. The article analyzes the definition of «green energy» and provides the authors’ own definition of this term. The key aspects of green energy are defined, among which the reduction of greenhouse gas emissions, energy security, sustainable development and environmental safety should be highlighted. The main factors influencing the development of green energy in Ukraine, such as economic instability, war, regulatory barriers and changes in tariff policy, are identified. The impact of green energy on the economy of Ukraine is studied. As a result, a positive impact on such aspects as the economic potential of green energy, energy independence and security has been identified. The government programs and plans to stimulate the development of green energy, in particular the Energy Strategy of Ukraine for the period up to 2050 and the National action plan on energy efficiency and renewable energy, are analyzed. The main directions of introduction of the latest technologies in green energy, in particular solar and wind energy, energy storage systems and Smart Grid technologies, have been identified. The use of these technologies will help improve the efficiency and reliability of energy systems. Therefore, green energy should be considered a key area of sustainable development, the main idea of which is to maximize the use of renewable energy sources and reduce emissions of harmful substances during the production and consumption of energy.

Independence through leapfrogging: Energy transitions in Eswatini

AbstractThe need to swiftly transition to clean energy and expand electricity access is pressing; however, the goals of SDG7 are unlikely to be met by 2030, with the UN specifically mentioning land‐locked states as being off track. Through the conceptual lens of ‘leapfrogging’ fossil fuels, straight from traditional fuel sources to renewable energy, this paper discusses the relationship between electricity production and independence. It makes a conceptual contribution by introducing the term ‘energy independence through leapfrogging’, which is used to characterise the process of reducing dependency on another country – in the case of Eswatini, electricity is supplied by South Africa. Drawing from empirical research in Eswatini involving document analysis and in‐depth interviews, this paper discusses the potential for the country to move from being primarily an importer of electricity to generating its own supply from renewable sources. With Eswatini's current electricity supply from South Africa at risk/expiring in 2025, this research argues that the country faces a moment of opportunity for Eswatini to build further generation capacity, with a recommended focus on solar energy. The paper identifies potential challenges/barriers to this energy transition, examining power generation, storage, maintenance and affordability as key areas for intervention. Drawing on previous theories of electricity access, the paper argues that ‘access’ to electricity requires consideration of the affordability and reliability of energy systems. Eswatini is an understudied geographical area, and this paper makes a contribution to the literature on energy transitions by examining the specific circumstances attending this transition and examining these with relevance for other land‐locked nations in sub‐Saharan Africa.

Identification of PV Fault Classes Using Intelligent Method KNN (K-Nearest Neighbours)

Throughout many developing nations of our humble planet, renewable energy is a hot topic. Every country at this very moment is trying to move away from fossil fuels like petrol to complete renewable energy sources especially Photovoltaic systems. The reliability and efficiency of renewable energy systems is now a frequent topic of discussion. Like all systems of production, renewable energy systems are subject to failures and defects in their normal operating functions with regards to the amount of power output. These systems break down and deteriorate during the period of their operation. This is why a system of diagnostic is required whose many objectives is to provide indicators with the given valuables like temperature, solar irradiation, voltage and current output to detect the faults and thus maintain the energy production at optimum. The work in progress relates to the diagnostic of faults in the PV systems using artificial intelligent methods particularly the K-nearest Neighbour algorithm.

Dual Axis Solar Tracking System with Weather Monitoring System

This paper presents the design and implementation of a solar tracking system integrated with weather monitoring capabilities. The system is designed to maximize the efficiency of solar panels by continuously adjusting their orientation to track the sun's position throughout the day. In addition to solar tracking, the system incorporates weather monitoring sensors to collect real-time data on environmental conditions such as cloud cover, wind speed, temperature and light intensity. This data is utilized to dynamically adjust the solar panel's position to optimize energy production and system performance in varying weather conditions. The integration of dual-axis tracking with weather monitoring enhances the overall efficiency and reliability of solar energy systems, making them more adaptable to changing environmental factors and increasing their potential for renewable energy generation.

СОВРЕМЕННЫЕ ТЕХНОЛОГИИ ХРАНЕНИЯ ЭЛЕКТРОЭНЕРГИИ ДЛЯ ЭНЕРГОСИСТЕМ БУДУЩЕГО

Modern technologies for storing electricity are a key element for ensuring the reliability and sustainability of energy systems of the future, especially in the context of a growing share of renew-able energy sources (RES). This article discusses the main types of energy storage devices, such as elec-trochemical, mechanical, thermal and electromagnetic systems, their advantages, disadvantages and im-plementation prospects. Particular attention is paid to the development of new materials and technologies to improve the efficiency and economic feasibility of these solutions.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles.

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

Ensuring the reliability of energy systems through the application of a new method for introducing soil-replacement mixtures during the installation of deep grounding devices

This paper discusses a method for using mixtures to optimize the electrical parameters of grounding devices during the installation of vertical composite ground electrodes. The authors propose a design for the coupling and tip for vertical composite ground electrodes. Various techniques for artificially reducing the resistance of the grounding circuit are examined. Results from vertical electrode soundings of the soil at grounding locations are presented. The proposed method allows for the introduction of a mixture simultaneously with the vertical composite grounding electrode, including designs for the coupling, tip, and auxiliary device. Experimental studies of the proposed design have been conducted, and results measuring the resistance to current spread of such grounding devices are presented for both standard and proposed couplings.

Može li veštačka inteligencija da doprinese pravednoj energetskoj tranziciji?

The level of fairness of the energy transition mainly depends on the balance of the energy trilemma in the observed country. Artificial intelligence (AI) and machine learning have the potential to improve the overall performance of the energy system. The advantage of these technologies is that they can be applied in the energy production and consumption sectors, which can maximize energy efficiency and thus reduce the level of primary energy consumption. Likewise, AI can be used to increase the level of flexibility of the power system, enabling greater inclusion of renewable sources. All this affects the reduction of emissions, which represents one of the most important norms in reaching the goals of decarbonization of the economy. In addition to the above, AI could be used to improve the reliability of energy systems and reduce the overall costs of their exploitation. There are also significant limitations in the application of artificial intelligence. First of all, there is currently a rather big problem with the adequate workforce needed for further development, which creates a certain "bottleneck" in the application in the energy sector. Also, AI's electricity consumption is not negligible, which potentially represents an additional load on production capacities. The goal of the research is to make an overview of the current applications of AI in the energy sector and to give a critical review of the impact on energy justice using those specific examples. In the research, communication with the publicly available tool based on artificial intelligence, ChatGPT, will be used in order to reach a certain set of information about the topic of interest. In this way, AI will be directly involved in the research in such a way that a set of specific questions will be asked in ChatGPT, and the answers obtained will be integral part of the final paper.

AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization

This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization. The study systematically examines the intersection of AI and renewable energy, highlighting the emergence of AI-driven solutions and their impact on enhancing the efficiency, reliability, and sustainability of renewable energy systems. The review begins with an overview of renewable energy and its growing importance in the global energy mix, emphasizing the critical role of AI in this sector. It then delves into the methodological approach, outlining the research strategy and criteria for selecting relevant AI and data science studies in renewable energy. This includes a detailed analysis of data collection and synthesis techniques used to identify key AI innovations and trends in solar and wind energy optimization. The core of the review comprises an extensive literature survey on AI applications in solar and wind energy systems. It covers fundamental principles of AI in renewable energy, state-of-the-art data science techniques, and emerging trends such as novel AI algorithms and their integration into renewable energy grids. The study evaluates the technological, economic, and environmental impacts of AI in renewable energy, addressing challenges and proposing solutions. Furthermore, the review discusses the role of standards and regulatory frameworks in AI-driven renewable energy and the implications for stakeholders. It concludes with a summary of AI's role in enhancing renewable energy, future prospects, and recommendations for industry leaders and policymakers. This review provides a thorough understanding of the current state and future potential of AI in renewable energy, offering valuable insights for researchers, industry professionals, and policymakers engaged in the field of sustainable energy.

Structural and Economic Approach to the Selection of Peak Regulating Capacities at Thermal Power Plants

The output of power unit No 2 of the Belarusian NPP to the level of neutron power development of 100 % of the nominal level has changed the structure of generation of the Unified Energy System (UES) of Belarus. At the current loads of the inter-heating period of 2023, the share of a nuclear power plant with two power units operating at full capacity in the daily generation structure of the Republic is about 50 %. In order to balance the UES of Belarus during the operation of two power units of a nuclear power plant at night, the application of adjustment regulatory measures that exceed the available capabilities is required. The almost complete use of the adjusting range of the CHP will forcibly cause, on a larger scale, to the shutdown of the blocks during the load failures at night, followed by the start-up from the non-cooled and hot statuses. These operating modes of generating equipment are inevitable in the current conditions of the electricity market, which has led to the emergence of most complex tasks to assess the economic and technical efficiency of involving or another equipment of power plants in such modes. The paper proposes an approach to solving the problem of achieving an effective combination of reliability and efficiency of energy systems on the example of gas turbines and CCGT power units. A generalized desirability function has been obtained, which includes a number of parameters for choosing a rational relationship and mutual influence between the reliability and efficiency of electricity production with the participation of thermal power plant equipment in regulating the daily load schedule. The proposed mathematical model is an effective means of analyzing the suitability of equipment for use in the required operating modes.

Development of an innovative model for the inter-integration of the architecture of the intelligent computer environment of critical infrastructure facilities of the railway

The object of research is the processes of intelligent management of the computer environment of data objects of the critical infrastructure of railway transport. When developing an innovative model of deep mutual integration of the architecture of an intelligent computer environment for the management of objects in the power supply system of railway transport, the only urgent task is to solve the problems of efficient and reliable power supply of electricity for train traction to ensure the transportation process. The approaches, mathematical models and methods that became the basis for the creation of an innovative model and a new structure of the management system, the integration of the architecture of the computer environment of data of critical objects of the infrastructure of railway transport, meet the requirements of modernity and the strategy of sustainable development of the transport infrastructure. Management of an intelligent computer environment in the context of the railway power supply system is a complex process that includes the use of various technologies and strategies to optimize the functioning of systems, increase reliability, ensure efficient use of electricity, and ensure the transportation process. The basis of the creation of an intelligent computer environment is the principle of a single informational and synchronous space for the formation of primary information, which is an important concept in the development of intelligent control systems for the power supply system of railway transport. This principle requires that all parameters and data that are collected from different systems of power supply facilities should be combined in a single information space and be available for analysis and management in real time. This principle creates the basis for innovative solutions in the field of railway power supply system management, and the use of the Internet of Things, artificial intelligence, machine learning and deep learning allows the development of systems that meet modern requirements for the efficiency and reliability of energy systems. The use of the approach of deep mutual integration of the architecture of the computer environment is key in the possibility of automating the processes of data collection and analysis, as well as in improving the interaction between the components of the systems of power supply facilities of railway transport, the reliability and efficiency of the system, which makes it more flexible and adaptive to changes in load and working conditions. The research presented in the work can be used in practice in organizations, structural units and at the levels of the management system of critical infrastructure objects in railway transport, transport sector enterprises, which will allow a quick response to an emergency situation and switch to backup modes, ensuring reliability and availability of power supply in conditions of challenges.

Application of Underwater Constant Pressure Compressed Air Energy Storage Technology in The Direction of Hydrogen Production

With the growing global demand for renewable energy to cope with climate change and energy security issues, underwater compressed air energy storage technology has gradually attracted attention. At the same time, hydrogen production process, as an important support for green energy transition, is becoming one of the key links to achieve clean energy generation and storage. This paper aims to explore the connection between underwater compressed air energy storage technology and hydrogen production process, and put forward a device combining underwater compressed air energy storage and hydrogen production, so as to solve the problems of low efficiency and serious pollution in the traditional hydrogen production industry. In the past, scholars focused on microcosmic research in the direction of hydrogen production, and tended to the direction of catalyst. Based on this, this paper will study the case of underwater compressed air energy storage device, and conceive a new hydrogen production device to promote the collaborative application of sustainable energy and improve the integration efficiency and reliability of energy system.

Mixformer: Mixture transformer with hierarchical context for spatio-temporal wind speed forecasting

Wind energy has attracted more and more attention due to its sustainability and pollution-free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of great importance for fully harvesting wind energy and ensuring the stability and reliability of wind energy systems. By accurately predicting the future wind speed, we can make efficient power schedules and consequently improve the utilization of wind power. Although existing forecasting methods have achieved satisfactory wind forecasting performance, they may still fail to discover the intricate spatio-temporal dependencies obscured in entangled patterns. To resolve this issue and further improve the prediction accuracy, we propose Mixformer, a novel mixture Transformer with hierarchical context for spatio-temporal wind speed forecasting. Mixformer first applies seasonal-trend decomposition, and then employs an MLP to predict the trend part and an attention model to predict the seasonal part. Besides, Mixformer innovatively proposes a spatio-temporal Gaussian mixture attention (ST-GMA) layer, which fuses the periodic temporal and long-term spatial context. To capture intricate long-term spatial characteristics, Mixformer extracts global information by the Dynamic Time Warping algorithm. Moreover, with the ST-GMA layer, Mixformer builds a hierarchical encoder–decoder architecture to fully utilize the context at different scales for seasonal forecasting and further enhance the spatio-temporal modeling. Empirical experiments on four real-world benchmark datasets show that Mixformer yields the lowest prediction errors with the MAE scores of 1.66, 1.68, 3.14 and 0.70 and the RMSE scores of 2.07, 2.01, 3.99 and 0.98, and improves the prediction accuracy by 8.43%, 5.36%, 4.14%, 6.57% in term of MAE scores and 9.18%, 6.92%, 4.01%, 5.75% in term of RMSE scores compared to state-of-the-art methods, which demonstrates the superiority of Mixformer. In a nutshell, Mixformer presents a promising method for spatio-temporal wind speed forecasting and may achieve desirable performance in real-world applications of wind power systems.

Fault Diagnosis for PV Modules Based on AlexNet and Symmetrized Dot Pattern

Faults in solar photovoltaic (PV) modules often result from component damage, leading to voltage fluctuations and decreased stability in the power system. In this study, the original voltage signals of different PV modules show little variation. Therefore, a solution that combines symmetrized dot pattern (SDP) and AlexNet for fault detection in PV modules was proposed. This solution investigates three common faults: poor welding, cracking, and bypass diode failure, which can be applied to fault-free modules. First, a high-frequency signal was input into the PV module, and the raw signal was captured using an NI PXI-5105 high-speed data acquisition card. Next, we used SDP to process the signal and create images with specific snowflake-like features. These images were used as a basis for fault diagnosis. Finally, deep-learning algorithms were used to perform status detection on the PV module. This research also used 3200 training samples and 800 test samples (200 for each type) to evaluate a new method for diagnosing faults in PV modules. The results show that the accuracy of the new method reached 99.8%, surpassing traditional convolutional neural networks (CNN) and extension neural networks (ENN), whose accuracies were 99.5% and 91.75%, respectively. Furthermore, this study compares the proposed method with more traditional numerical fault diagnosis methods. SDP effectively extracts fault signals and presents them as images. With AlexNet used for fault identification, the method excels in accuracy, training time, and testing time, thereby enhancing the stability and reliability of future energy systems.

Fuzzy control of synchronous buck converters utilizing fuzzy inference system for renewable energy applications

<p>In the present research, an innovative fuzzy control approach is developed specifically for synchronous buck converters utilized in renewable energy applications. The proposed control strategy effectively manages load changes, nonlinear loads, and input voltage variations while improving both stability and transient response. The method employs a fuzzy inference system (FIS) that integrates adaptive control, feedforward control, and multivariable control to guarantee optimal performance under a wide range of operating conditions. The design of the control scheme involves formulating a rule base connecting input variables to an output variable, which signifies the duty cycle of the switching signal. The rule base is configured to dynamically modify control rules and membership functions in accordance with load conditions, input voltage fluctuations, and other contributing factors. The performance of the control scheme is evaluated in comparison to conventional techniques, such as proportional integral derivative (PID) control. Results indicate that the advanced fuzzy control approach surpasses traditional methods in terms of voltage regulation, stability, and transient response, particularly when faced with variable load conditions and input voltage changes. As a result, this control scheme is highly compatible with renewable energy systems, encompassing solar and wind power installations where input voltage and load conditions may experience considerable fluctuations. This research highlights the potential of the proposed fuzzy control approach to significantly enhance the performance and reliability of renewable energy systems.</p>

ENSURING THE FUTURE OF RENEWABLE ENERGY: A CRITICAL REVIEW OF RELIABILITY ENGINEERING APPLICATIONS IN RENEWABLE ENERGY SYSTEMS

As the global shift toward sustainable energy intensifies, the reliability and robustness of renewable energy systems become paramount. This research article offers a comprehensive examination of the pivotal role that reliability engineering plays in optimizing the efficiency, longevity, and dependability of these renewable systems. Delving into varied energy domains, from solar and wind to geothermal and bioenergy, the study elucidates the challenges and breakthroughs in ensuring consistent energy outputs amidst the inherent uncertainties of renewable sources. The study exploration also highlights the innovative intersection of emerging technologies, such as artificial intelligence and predictive maintenance, in augmenting the reliability of these green energy systems. Through real-world case studies, the research paper provides insights into the tangible benefits and lessons drawn from effective reliability engineering applications. Conclusively, the paper posits actionable recommendations for both policymakers and engineers, emphasizing the indispensable nature of reliability engineering in shaping a resilient renewable energy future. This study serves as a seminal reference for stakeholders aiming to enhance the sustainability and dependability of the next generation of renewable energy systems.

Impact of climate simulation resolutions on future energy system reliability assessment: A Texas case study

Impact of climate simulation resolutions on future energy system reliability assessment: A Texas case study

Regional integrated energy system reliability and low carbon joint planning considering multiple uncertainties

Regional integrated energy system reliability and low carbon joint planning considering multiple uncertainties

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection.