System Performance Efficiency Research Articles

PEMFC Gas-Feeding Control: Critical Insights and Review

Proton exchange membrane fuel cells (PEMFCs) are currently a relatively mature type of hydrogen energy device due to their high efficiency and low noise compared to traditional power devices. However, there are still challenges that hinder the large-scale application of PEMFCs. One key challenge lies in the gas supply system, which is a complex, coupled nonlinear system. Therefore, an effective control strategy is essential for the efficient and stable operation of the gas control system. This paper aims to provide a comprehensive and systematic overview of the control strategies for PEMFC anode and cathode supply systems based on an analysis of 182 papers. The review covers modern control theories and optimization algorithms, including their design, objectives, performance, applications, and so on. Additionally, the advantages and disadvantages of these control methods are thoroughly evaluated and summarized.

Simplified bit-class probabilistic shaping strategy based on PDM systems.

This study aims to present a simplified bit-class probabilistic shaping (PS) strategy based on power domain multiplexing (PDM) systems. In this scheme, we employ bit-weighted distribution matching (BWDM) to achieve PS. By ingeniously applying the process of signal superposition, the low-complexity bit-class PS scheme requires encoding of only a small number of bits in the low-power two quadrature phase-shift keying (QPSK) signal, enhancing the quality of the transmitted signal with simple operations. At the receiver, to ensure the transmission performance of the system while minimizing computational complexity and enhancing system operational efficiency, we implemented the region decision method for demodulation. The proposed scheme was experimentally validated on a 2 km 7-core fiber, achieving accurate transmission of signals at a net rate of 103.6 Gb/s. The experimental results indicate that, when compared to the uniform 16-quadrature amplitude modulation (16QAM) signal and the PDM-orthogonal frequency division multiplexing (OFDM) signal without PS, the receiving sensitivity of the PS-16QAM signal in our scheme can gain 0.4 dB and 0.33 dB at a bit error rate (BER) of 3.8 × 10-3 under the same net rate.

CHARACTERISTICS OF THE DEVELOPMENT OF THE GAS SUPPLY SYSTEM IN THE REPUBLIC OF ARMENIA

Gas distribution and transportation networks make up the gas supply system. Providing natural gas to the public and the economy is the primary objective of the gas delivery system. Every nation's gas supply network is strategically vital since the cost of natural gas directly impacts the goods and services that are produced and rendered there. It is true that developed nations are looking for innovative technology for natural gas extraction because it is a non-renewable resource. Founded during the Soviet era and throughout multiple phases of development, the Republic of Armenia's gas supply system is a component of the worldwide network. The Public Services Regulatory Commission and the Armenian government, through the Ministry of Territorial Administration and Infrastructure, oversee the country's gas supply industry. The article discusses the Republic of Armenia's domestic natural gas market's supply and sales, its background and present state, investment initiatives, and features that increase the system's operational efficiency.

An Ultra‐Short‐Term Multi‐Step Prediction Model for Wind Power Based on Sparrow Search Algorithm, Variational Mode Decomposition, Gated Recurrent Unit, and Support Vector Regression

ABSTRACTAccurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications.

Methods and equipment for analysis and diagnosis of marine engines during navigation

ABSTRACT Expert diagnostic systems are required for the development of intelligent marine engines and other ship systems. Measurement and analysis of marine engine parameters are fundamental in ensuring the efficient operation of these key propulsion systems. This paper introduces a novel measurement device and method capable of real-time monitoring of cylinder pressure and emissions in marine engines. The experimental procedure involved measuring critical engine parameters and emissions, such as NOx, CO2, and particulate matter, under various operating conditions. The device provided accurate real-time data acquisition, enabling detailed analysis of combustion processes and emission profiles under various operating conditions. Furthermore, a numerical model was developed, like digital twins, which utilized the measured data to simulate engine performance, predict potential failures, and optimize operational parameters to enhance efficiency and reduce environmental impact. The two most important parameters: the indicated cylinder pressure and NOx emissions were simulated and validated. Measured NOx was measured 889 ppm at 90% and simulated results was 985 ppm, which is 10% difference. Specific fuel consumption has less than 1% difference between measured and simulated data. Novelty of this work is the methodology used in the testing and calibration processes. Current actual parameters of engine operation are monitored, based on which a comparative analysis is made in real time. Previous diagnostic control systems did not provide diagnostics, comparative analysis and optimization in real time. The integration with digital twin allowed potential wear and tear predictions, fuel consumption optimization, and emission reduction strategies, showcasing the potential of this technology to improve marine engine diagnostics and maintenance. The device and its virtual counterpart worked well together to provide a complete understanding of the engine’s response. This facilitates proactive maintenance strategies and contributes to eco-efficiency in the maritime sector.

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Development of a Data-driven Decision Support System for the Efficient Operation of the Research Reactor Secondary System

The High-flux Advanced Neutron Application Reactor (HANARO) is a research reactor in the Republic of Korea. The secondary cooling system of HANARO releases heat into the atmosphere via cooling towers and fans. Because a part of the system is exposed to the external atmosphere, the behavior of the secondary cooling system is coupled with atmospheric conditions. However, no procedure exists to reflect such coupling, and the cooling fans are empirically controlled by the operators. This empirical decision-making may increase the workload and reduce operational efficiency; therefore, the cooling fan operational schemes must be improved. In this study, a decision support system is developed based on two artificial intelligence (AI) models to aid operators’ decision-making in controlling the cooling fans. During model development, data augmentation was applied to alleviate data imbalances. Additionally, a graphical user interface-based decision support system prototype was developed based on the models to provide operators with more comprehensive summaries of the information related to cooling-fan operations and outputs from the AI models. The models demonstrated acceptable performance on the testing data. The prototype system was installed in the main control room of HANARO and evaluated for its usability improvements and feasibility in practical applications.

Transitive reasoning: A high-performance computing model for significant pattern discovery in cognitive IoT sensor network

Current research on the Internet of Things (IoT) has given rise to a new field of study called cognitive IoT (CIoT), which aims to incorporate cognition into the designs of IoT systems. Consequently, the CIoT inherits specific attributes and challenges from IoT. The CIoT applications generate vast, diverse, constantly changing, and time-dependent data due to the billions of devices involved. The efficient operation of these CIoT systems requires the extraction of valuable insights from vast data sources in a computationally efficient manner. Therefore, this study proposes transitive reasoning to glean significant concepts and patterns from a 21.25-year environmental dataset. To reduce the effects of missing entries, the proposed methodology includes a grouping of data using probabilistic clustering and applying total variance regularization in the alternate direction method of multipliers (ADMM) to regularize the sensory data. As a result, noisy entries will be less conspicuous. Afterward, it calculates the transitional plausibility value for each cluster using the transited value and then turns it into binary data to create concept lattices. In addition, each concept that is formed is assigned a weight, and the concept with the largest transitive strength value is chosen, followed by calculating the mean value. Therefore, this pattern is seen as significant. Experimental results on 21.25-year environmental data show an accuracy of over 99.5%, outperforming competing methods, as shown by cross-validation using multiple metrics.

Development of the model and improvement of the method of automated control of steam turbine parameters to minimize the power imbalance in the energy system to increase its efficiency

The object of research is the process of regulating power imbalances due to automated control of steam turbine parameters. The work solves the problem of minimizing power imbalances in the electric power system by developing a model of automated control of steam turbine parameters. This will ensure high-quality regulation of frequency and power, increase the efficiency and stability of the electric power system, and provide a new management method for reliable power supply to consumers. The paper analyzes existing models and methods of power imbalance regulation, develops a dynamic model of automated control of K-300-240 steam turbines, which includes a mechanical-hydraulic system, a steam boiler and a steam superheater. As a result of the study, an improved power regulation method was proposed, which ensures efficient operation of the electric power system. The evaluation of the effectiveness of the regulation of imbalances was carried out on the basis of the proposed criterion of the efficiency of electricity supply and consumption, which is based on the convolution of partial criteria into the general criterion of the efficiency of electricity supply. The following criteria were proposed as partial criteria for electricity supply and consumption: volume criterion, quality criterion, and electricity supply efficiency criterion. The research results indicate the need for a reserve on each steam turbine in the amount of 10 % of its nominal power, which is explained by the assessment of the efficiency of electricity supply among the considered modes of operation of the systems. The presence of a power reserve on each steam turbine in the amount of 10 % of their nominal power ensures the most efficient power supply within the considered modes of operation of the power system, taking into account disturbances and as an imbalance of power generation and power consumption. The obtained research results can be applied in the strategy of primary regulation of power imbalances in the electric power system, thanks to the creation of a power reserve in the amount of 10 % of the nominal power on each steam turbine, and the organization of automated control of steam turbine parameters.

Very short-term wind power forecasting considering static data: An improved transformer model

The randomness and fluctuations in wind power generation present significant challenges for grid and wind farm dispatching. Accurate very short-term wind power forecasting (WPF) is therefore essential for the efficient operation of modern power systems. Data-driven models, such as Transformers, have demonstrated their effectiveness in WPF due to their ability to efficiently capture global features in long sequences. However, limited research has examined the impact of incorporating static data into WPF, which may limit forecasting accuracy. This paper proposes a Temporal Fusion Transformer forecasting model to address this challenge. This approach employs static data as the input features for the model. The model includes feature selection through a variable selection network and employs a specialized temporal fusion decoder to learn effectively from these static features. The case results show that the results of the proposed model are more accurate than the state-of-the-art methods, reducing MAPE by at least 1.32%, RMSE by 0.0091, and improving R2 by 0.035 in case studies. Additionally, the model maintains a manageable computational burden, underscoring its practical applicability.

Advancements in Network Attached Storage (NAS) for Mobile Devices: Technological Developments and Performance Assessment

Network-attached storage (NAS) technology has emerged as a vital solution in the evolving landscape of data management and storage. With the exponential growth of data and the increasing demand for efficient, scalable storage solutions, NAS systems offer a centralized, cost-effective, and flexible alternative to traditional storage methods. This paper explores the application and advantages of NAS technology in various scenarios, from home and small business environments to large enterprises. It highlights the key strengths of NAS systems in optimizing data storage, ensuring data security, and facilitating cross-device sharing. The study delves into the architectural components of NAS, including file systems, RAID configurations, and network protocols, to demonstrate how these technologies enhance storage performance and reliability. Technical details on NAS system design, such as the integration of advanced file systems and user-level networking technologies, are discussed to showcase their impact on data transfer efficiency and overall system performance. Experimental results validate the effectiveness of NAS in different environments, illustrating its ability to provide high availability, data redundancy, and low-latency data access. The findings underscore NAS technology’s potential as a versatile and scalable storage solution, with recommendations for future research focused on developing new file systems, optimizing network protocols, and exploring edge computing integrations to further enhance NAS capabilities.

Levy flight elitism-enhanced salp swarm algorithms for solving the ORPD problem

The optimal reactive power dispatch (ORPD) challenge is crucial for ensuring the efficient and reliable operation of power systems, hinging on the effective coordination of multiple devices.As a result, solving the ORPD problem involves tackling a complex nonlinear optimization challenge that demands advanced computational algorithms to determine the optimal solution. This article introduces an enhanced version of the modified algorithm, called L-E-SSA, which is inspired by the coordinated movement of a swarm led by a single member (salp swarm algorithm SSA), incorporating Levy Flight elitism, to effectively solve the ORPD problem. This study aims to reduce power losses (Ploss) and total voltage deviation (TVD) by optimizing the control variables. Numerical simulations are carried out on IEEE (30-bus) and IEEE (118-bus) power systems. To evaluate the performance and effectiveness of the modified algorithm (L-E-SSA). The results indicate that the L-E-SSA algorithm delivered strong performance and secured a competitive ranking through statistical analysis. Simulations demonstrate that the proposed method has the potential to be a highly effective solution for the ORPD problem.

A secure and efficient data aggregation scheme for cloud–edge collaborative smart meters

Smart meters are part of the Advanced Measurement Infrastructure (AMI) system in the smart grid. It facilitates data transfer between consumers and electricity suppliers (ES). However, the mass deployment of smart meters (SM) brings heavy overhead to grid operation and poses serious privacy threats. To this end, this paper proposes a secure and efficient data aggregation scheme of cloud–edge collaboration smart meters. At first, we standardize the users’ historical electricity load features and use the improved K-Means clustering algorithm to calculate the Euclidean distance between feature vectors to obtain the classification results of users’ load features. On this basis, ES generates relevant parameters to encrypt meter data and protect users’ data privacy based on classification results. The aggregator (Ag) performs the data aggregation, generates the overall signature using the Schnorr aggregation signature method, and sends it to the cloud server (CS). The ES queries the CS to obtain data and parses it to realize the customer billing service. Meanwhile, this paper executes a series of experiments, and the results show that the proposed scheme exhibits significant advantages in privacy protection and system operation efficiency.

Effective passenger flow congestion propagation model for multi-mode rail transit networks

ABSTRACT During peak passenger flow periods, congestion propagation directly affects the operational safety and efficiency of multi-mode rail transit interconnections. By analyzing the key factors affecting congestion propagation, such as the train stop schedule, and considering parameters such as the basic reproduction number and propagation threshold, this study proposes a multi-mode rail transit susceptible-infected-recovered-susceptible (MRT-SIRS) epidemic model to analyze passenger flow congestion propagation. Simulation experiments and sensitivity analyses using data from the multi-mode rail transit in Beijing, China, were conducted to examine the influence mechanism of key factors on congestion propagation. The degree of influence of each factor was investigated using Gray correlation analysis. Each key factor, including the propagation and recovery rates, influences congestion propagation differently. The results of this study may provide theoretical support for the efficient operation and management of multi-mode rail transit systems.

High‐Accuracy Liquid Flow Monitoring via Triboelectric Nanogenerator Combined with Bionic Design and Common‐Mode Interference Suppression

AbstractIn the development of smart cities, accurate liquid flow monitoring is essential for the efficient operation of water supply systems. Current flow sensors often face limitations in sensitivity and environmental adaptability, affecting measurement accuracy, and restricting their application in smart city infrastructure. To address these challenges, this study proposes a high‐accuracy flow monitoring method. Specifically, by combining the bionic design with advanced signal processing techniques, the sensitivity and anti‐interference ability are improved, respectively, to enhance the measurement accuracy. Based on this method, a self‐powered flow sensor (SPFS) is developed using noncontact triboelectric nanogenerators (NC‐TENGs) as the sensing unit. The SPFS achieves a sensitivity of 2.07 Hz L−1 min−1 and improves the signal‐to‐noise ratio by more than 13 times over the initial sensing signal. In addition, an intelligent system is developed to accurately measure water resources. The maximum flow rate error rate is less than 0.97% compared to commercial flow sensors. The SPFS demonstrates higher sensitivity and accuracy compared to the existing TENG flow sensors. This study addresses the limitations of existing flow sensors and pioneers a novel solution for enhanced water resource management in smart cities.

Co-pyrolysis of furniture wood with mixed plastics and waste tyres: assessment of synergistic effect on biofuel yield and product characterization under different blend ratio

Pyrolysis of waste furniture wood, mixed plastics and waste tyres was examined separately and in different combinations from the perspective of improved value products and energy production. The effect of different combinations of furniture wood, plastics and tyres on the product distribution during co-pyrolysis was analyzed. The experimental work throughout this study was performed at a temperature of 500 °C. Prior to the pyrolysis experiments, thermogravimetric analysis was done to assess the thermal degradation behavior of all selected feedstocks. During individual pyrolysis, mixed plastic wastes produced 70.6 wt% of pyrolysis liquid, which is 37.9% and 33.4% more than furniture wood and waste tyres. During co-pyrolysis, a binary blend of mixed plastics and waste tyres produced 63.3 wt% of pyrolysis liquid, which is 7.65% more than the logarithmic mean value, indicating a positive synergistic interaction on liquid production. The liquid yield of the ternary blend was observed to be as low as 54.4 wt% due to the lower volatiles present in the blend. The presence of volatiles in the feedstock is correlated with the production of liquids by individual and co-pyrolysis. For feed-flexible, more efficient, and cleaner operating systems, the increased liquid production offers crucial information. With the use of Fourier transform infrared spectroscopy (FT-IR) and gas chromatography/mass spectroscopy (GC–MS), the impact of different combinations on product characterization was investigated. The characterization study of the pyrolysis liquid obtained from mixed plastics showed the presence of different aromatic and aliphatic compounds. The findings offered viable options for efficiently utilizing waste materials, particularly plastics and tyres to improve the quality and substance of products.

A Platform of Federated Learning Management for Enhanced Mobile Collaboration

Federated learning (FL) has emerged as a crucial technology in today’s data-centric environment, enabling decentralized machine learning while safeguarding user privacy. This study introduces “Federated Learning ML Operations (FedOps) Mobile”, a novel FL framework optimized for the dynamic and heterogeneous ecosystem of mobile devices. FedOps Mobile addresses the inherent challenges of FL—such as system scalability, device heterogeneity, and operational efficiency—through advanced on-device training using TensorFlow Lite and CoreML. The framework’s innovative approach includes sophisticated client selection mechanisms that assess device readiness and capabilities, ensuring equitable and efficient participation across the network. Additionally, FedOps Mobile leverages remote device control for seamless task management and continuous learning, all without compromising the user experience. The main contribution of this study is the demonstration that federated learning across heterogeneous devices, especially those using different operating systems, can be both practical and efficient using the FedOps Mobile framework. This was validated through experiments that evaluated three key areas: operational efficiency, model personalization, and resource optimization in multi-device settings. The results showed that the proposed method excels in client selection, energy consumption, and model optimization, establishing a new benchmark for federated learning in diverse and complex environments.

Healthiness and Safety of Smart Environments through Edge Intelligence and Internet of Things Technologies

Smart environments exploit rising technologies like Internet of Things (IoT) and edge intelligence (EI) to achieve unseen effectiveness and efficiency in every tasks, including air sanitization. The latter represents a key preventative measure–made even more evident by the COVID-19 pandemic–to significantly reduce disease transmission and create healthier and safer indoor spaces, for the sake of its occupants. Therefore, in this paper, we present an IoT-based system aimed at the continuous monitoring of the air quality and, through EI techniques, at the proactively activation of ozone lamps, while ensuring safety in sanitization. Indeed, these devices ensure extreme effectiveness in killing viruses and bacteria but, due to ozone toxicity, they must be properly controlled with advanced technologies for preventing occupants from dangerous exposition as well as for ensuring system reliability, operational efficiency, and regulatory compliance.

Operational electricity cost reduction using real-time simulators

Ensuring the safe and cost-effective electricity transmission to consumers, along with the efficient and sustainable operation of power systems, has long been a primary objective for power system managers and operators. However, achieving optimal performance in a modern power system requires timely planning and operational optimal routines to be run within the realm of real-time simulation programs, and sometimes, the divergence becomes a problem with these developed routines. Despite extensive research aimed at advancing these objectives, the growing complexity of networks and their constraints has often led to the oversight of simultaneously considering both technical and economic aspects during operation. In this paper, an innovative real-time framework is introduced to reduce the operational costs of a power system swiftly. This framework addresses various nonlinear constraints, such as transmission losses, generation-consumption balance, power device limitations, and transient stability constraints, across various operational scenarios within a real-time simulator. This novel approach leverages scattered search and intentional contingencies within the network to pinpoint the optimal operating point, taking into account various network dynamics, including Automatic Voltage Regulators (AVR), governors, and tap of the transformers. The proposed method offers a fast and robust approach to identifying the most effective operating conditions for a power system. It incorporates considerations for sudden contingencies and extends capabilities through parallel simulations. It not only facilitates pre-determined preventive actions but also enables the adjustment of control parameters during post-contingency periods, such as fine-tuning of the active and reactive power generation of generators and adjusting the tap settings of transformers within power networks. Since the network simulation can be executed on distributed computers, referred to as ’Global,’ it is possible to achieve global network optimization. This approach allows for the consideration of grid networks, including renewable energy sources with models distributed through PCs. Also, it accounts for the induction motor losses within all factories involved in the global simulation. The results obtained from simulating the proposed method on a commercial real-time simulator demonstrate the superior effectiveness of the proposed framework compared to the existing methodologies, highlighting its potential to enhance the operational efficiency and economic viability of power systems.