System Performance Efficiency Research Articles

The Impacts of Different Salinities on the CW-MFC System for Treating Concentrated Brine

This paper aims to comprehensively explore the performance and influencing factors of the constructed wetland–microbial fuel cell (CW-MFC) system when treating brine with different concentrations. The main objective is to determine how different salinity levels affect the operation and treatment efficiency of the CW-MFC system. The research results show that Bruguiera gymnorrhiza exhibits strong salt tolerance and can be used as a wetland plant for the CW-MFC system. The closed-circuit CW-MFC system with planted plants has the best performance, with a chemical oxygen demand (COD) removal rate of 84.8%, a total nitrogen (TN) removal rate of 68.12%, and a chloride ion (Cl−) removal rate of 29.96%. The maximum power density is 64.79% higher than that of the system without planted plants. The power generation performance of the system first increases and then decreases with the increase in salinity, while the internal resistance keeps decreasing. When the salinity is 2%, the power generation effect is the best, with an average output voltage of 617.3 ± 25.7 mV and a power density of 45.83 mW/m2. The removal rates of COD and TN are inhibited with the increase in salinity, while the removal rate of total phosphorus (TP) is not significantly affected. The microbial community grows well under salt stress, but its structure is different. When the salinity is 1%, the optimal distance between electrodes is 10 cm. Considering the pollutant removal performance, the optimal hydraulic retention time is 3 days, and considering the power generation performance, the optimal hydraulic retention time is 2 days. This research provides important value for improving the performance of the CW-MFC system in treating brine.

A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges

Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios.

Power system optimization dispatch for energy conservation and emission reduction

This paper examines the optimization of power system dispatching with a focus on energy conservation and emission reduction. Through analyzing current situations and challenges, it aims to propose effective strategies and methods to achieve efficient power system operation while protecting the environment. The article commences by detailing China's advancements and obstacles in energy conservation and emission reduction within its power system. It addresses issues such as the variability of renewable energy sources, efficiency constraints in thermal power generation units, the intricate nature of the power grid infrastructure, and the novel challenges introduced by reforms in the electricity market. Subsequently, the paper presents four major strategies: priority dispatching of clean energy, flexible dispatching of thermal power units, power grid structure optimization, and market mechanism introduction. Research findings, when integrated with practical case studies, demonstrate that the utilization of virtual power plants and advanced dispatching systems can markedly enhance the capacity for clean energy consumption, decrease the operational duration of thermal power units and the intensity of carbon emissions, while simultaneously improving the overall efficiency of power systems. This research holds significant importance for promoting green and sustainable development in the power industry

Spectral efficiency and secrecy enhancing scheme for IRS-aided SWIPT systems

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology for enabling efficient and scalable wireless communication within large-scale Internet of Things (IoT) networks. Despite its substantial potential, challenges related to spectral efficiency and overall system performance remain, underscoring the need for further advancements and innovations. Furthermore, the secure transmission of information is a crucial aspect that cannot be overlooked, as it guarantees the integrity and confidentiality of data exchanged within IoT networks. In this paper, we introduce a novel generalized spatial modulation (GSM) scheme specifically designed for intelligent reflecting surface (IRS)-Aided SWIPT systems. Moreover, the proposed scheme incorporates space-time block coding (STBC) to enhance performance. To strengthen the secrecy, a novel artificial noise (AN) generation strategy that effectively disrupts eavesdroppers while minimizing the impact on legitimate receivers is proposed. Additionally, the designed AN can be utilized by the energy-harvesting receiver to enhance its energy collection capabilities. We derived the average bit error probability (ABEP) of the system using the moment-generating function (MGF) approach and validated our analytical findings through rigorous simulations. The results obtained demonstrate convincingly that the proposed GSM scheme for IRS-Aided SWIPT systems significantly outperforms traditional methods, offering remarkable improvements in performance and efficiency.

Research into the Possibilities of Improving the Adhesion Properties of a Locomotive

Locomotives are important vehicles, which serve for towing wagons, i.e., trains. Many factors influence the safe and cost-effective operation of locomotives and trains in general. One of these factors is adhesion at the wheel/rail contact. The adhesion determines how much power the locomotive can deliver and how the braking system will ensure that the train stops. The main way to improve adhesion is to use sand at the wheel/rail contact point. The aim of this study is to improve the efficiency of the sand system of the locomotive. For this purpose, a new sand system nozzle mounting design was proposed. The newly proposed sanding system is equipped with a nozzle mounted to the axlebox unlike the original one, which uses the nozzle attached to the bogie frame. To compare the proposed and existing design, simulation calculations were performed in Simpack software 2024.3. For the simulation computation of the locomotive bogie, two types of railway tracks were chosen. A straight track section with two angular frequencies and three amplitudes of track irregularities was created, and a real track section corresponding to several kilometers of track was modeled in the Simpack software. During the simulations, it was determined that the proposed nozzle mounting design has a smaller amplitude of motion, compared to the existing one; therefore, there is a more accurate and efficient operation of the sand system. This in turn has a favorable effect on the adhesion of the wheel with the rail. It was found out that the newly designed sanding system has a significant positive economic effect regarding saving sand. There is no sand loss during sandblasting compared with the original sanding system. This directly relates to saving costs during locomotive operation.

Intelligent active and reactive power control using multi-layer neural network based MPPT controller for grid tied solar PV system under fault conditions

The integration of renewable energy sources, particularly grid-tied solar photovoltaic (PV) systems, into the modern power grid has become increasingly prevalent. However, ensuring the reliable and efficient operation of grid-tied PV systems under various grid conditions, including fault scenarios, poses a significant challenge. In the event of grid faults or disturbances, traditional control methods often fall short in maintaining stable and reliable operation. This paper introduces a multi-layer neural network (MLNN) based MPPT controller that adapts intelligently to grid fault conditions, mitigating the impact on the grid-tied PV system's performance and providing low voltage ride through (LVRT). The research employs a detailed simulation framework on MATLAB to validate the effectiveness of the proposed controller under fault conditions. The LVRT capability of the designed system was analyzed and validated according to Indian grid code. The proposed controller leverages its capacity to learn and make real-time decisions to optimize the active and reactive power outputs of the PV system as per the grid code. Simulation results demonstrate that the proposed controller not only improves the fault tolerance of grid-tied PV systems but also enhances their performance, ensuring a stable and continuous power supply in the face of grid disturbances.

Research on reachable set boundary of neutral system with various types of disturbances.

This study delves into neutral-type systems (NTSs), emphasizing the critical role of defining precise reachable set (RS) boundaries for safe and efficient system design and operation. The investigation notably addresses the challenges posed by time-varying delays, applying Lyapunov's direct method alongside advanced matrix inequality techniques to identify minimized and more accurate ellipsoidal boundaries of the RS in NTSs influenced by bounded and nonlinear disturbances. Our findings, verified through numerical simulations and comparisons with existing literature, demonstrate enhanced control and management capabilities for complex systems, thus underscoring the substantial theoretical and practical value of incorporating delay elements in NTSs.

Clustering- and statistic-based approach for detection and impact evaluation of faults in end-user substations of thermal energy systems

In response to climate change mitigation efforts, improving the efficiency of heat networks is becoming increasingly important. An efficient operation of energy systems depends on faultless performance. Following the need for effective fault detection and elimination methods, this study suggests a three-step workflow for increasing automation in managing defective substations on the user level within heat networks. The work focuses on a model region in northern Germany. The local heat network provides data in roughly hourly intervals, including the supply and return temperatures and the volume flow of the substations. Firstly, this study identifies common indicators of faults using k-means clustering analysis of the temperature data and expert knowledge: an exceeded return temperature level, very low cooling, and inverted temperature readings. With these indicators, the subsequent statistical identification approach confirms the successful detection of affected substations, with common diagnoses including disabled return temperature limitation units, defective motoric valves, and faults in the storage control. Lastly, the study evaluates the impact of faults on the system efficiency. Combining the temperature and the volume flow data, the workflow quantifies the negative influence of a fault, enabling the prioritization of fault elimination measures in practical application to enhance the overall system efficiency.

Development of an Optimized Neural Network Model using Hyperparameters Optimization for Electrical Load Prediction

Electrical load prediction has become essential to the efficient operation, control and management of modern electric power systems. Various machine learning prediction models have been developed for electrical load prediction, this includes Support Vector Regression, Fuzzy Logic and Neural Network (NN) modelling approaches. However, incorrect selection of model hyperparameters, which are parameters that affect the output of the prediction models, could result in low prediction accuracy of machine learning models. Hence, the development of an optimized NN model for electrical load prediction was presented in this study. Historical daily data of temperature, rainfall, relative humidity and windspeed for Osogbo, Nigeria was obtained from the National Aeronautics and Space Administration website; while electrical load data for the same location was collected from the Transmission Company of Nigeria. The data captured a period of five years (2017 to 2021). The NN models were developed with MATLAB R2022a software, and two hyperparameters, hidden layers and neuron counts, were optimized using the Bayesian optimization technique to enhance the quality of the models. The models were evaluated using mean absolute error (MAE), and root mean square error (RMSE). The MAE and RMSE for the non-optimized NN model were 6.5247 and 8.2725 respectively. Meanwhile, for the optimized NN model, the MAE and RMSE were 5.6571 and 7.4289 respectively. The obtained results show that the optimized NN performed better than the non-optimized NN models. Therefore, for more accurate load prediction, the method developed of this research is suggested for use by utility providers.

Enhancing scalable reconfigurable manufacturing systems through robust optimisation: energy efficiency and cost minimisation under uncertainty

Reconfigurable manufacturing systems are dynamic systems designed with scalable and flexible production capabilities to address changing market demands. This paper presents a novel multi-objective integer programming model aimed at optimising the configuration and capacity scalability of reconfigurable machine tools in uncertain environments. The model focuses on minimising three key objectives: total energy consumption, unused capacity, and total cost. It incorporates critical manufacturing constraints such as peak power thresholds and limited tool availability. To effectively manage uncertainty, particularly in demand fluctuations, a scenario-based robust optimisation approach is applied, striking a balance between solution robustness and model adaptability. A comprehensive case study demonstrates the model's effectiveness, comparing deterministic and uncertain solutions. Additionally, sensitivity analyses are performed on parameters such as peak power thresholds, risk coefficients, and infeasibility weights, highlighting their impact on system performance. The results provide insights into the efficient design and operation of scalable reconfigurable manufacturing systems under uncertainty, with recommendations for future research directions.

Methodological provisions to the formation and the ensuring of efficiency and reliability of district-distributed heating systems

This study proposes a methodology for solving two problems of optimal development of district heating systems: analysis of efficiency areas and reliability of heat supply. In solving both problems, we adopt a nodal approach, which allows us to get detailed results that are most suitable to the real-world conditions. Based on the proposed methods and models, we develop an algorithm for transforming existing district heating systems into district-distributed heating systems with prosumers implemented into the network to serve the loads that fall outside the range of efficient operation of a district heating system. Wherein, the distributed sector is formed based on a prosumer that has its own generation, covering part of its own heat load and providing an additional functional and time redundancy for the system. As a result, conclusions and directions for further research are formulated.

Development Model of Halal Order Management Systems (OMS) for Online Business Pet Shop in Indonesia

The rapid growth of the pet shop industry in Indonesia highlights the need for an efficient and halal-compliant operational system to meet the increasing demand for halal-certified products among Muslim consumers. This study proposes the development and implementation of a Halal Online Management System (OMS) that integrates real-time inventory management, order processing, shipment tracking, and compliance with halal standards. Using Soft Systems Methodology (SSM), the research addresses operational challenges, including stock errors, delivery inefficiencies, and the segregation of halal and non-halal products. The proposed system ensures a streamlined workflow while fostering transparency and trust among stakeholders. The implications of this system are significant for stakeholders: consumers benefit from assured halal compliance and enhanced service quality, employees experience greater efficiency through automated processes, and business owners gain a competitive advantage in a rapidly evolving market. Additionally, logistics providers benefit from improved shipment accuracy and operational collaboration. By aligning business practices with halal principles, the system not only boosts customer satisfaction but also reinforces ethical and religious values in the supply chain. This conceptual model has broader implications for advancing halal business practices and offers a scalable framework for similar industries. Further research is recommended to empirically validate the system’s impact on stakeholder engagement and business performance.

Research on Low-Voltage Ride-Through and Intelligent Optimization Control of Wind Turbines Based on Hybrid Power Prediction Models

This paper proposes a dual-loop back-to-back converter coordination control scheme with a DC-side voltage as the primary control target, along with a CROW unloading control strategy for low voltage ride-through (LVRT) capability enhancement. The feasibility and effectiveness of the proposed system topology and control strategy are verified through MATLAB/Simulink simulations. Furthermore, a hybrid short-term wind power prediction model based on data-driven and deep learning techniques (CEEMDAN-CNN-Transformer-XGBoost) is introduced in the wind turbine control system. The coordination control strategy seamlessly integrates wind power prediction, pitch angle adjustment, and the control system, embodying a predictive-driven intelligent optimization control approach. This method significantly improves prediction accuracy and stability, theoretically reduces unnecessary pitch angle adjustments, lowers mechanical stress, and enhances system adaptability in complex operating conditions. The research findings provide a valuable theoretical foundation and technical reference for the intelligent and efficient operation of wind power generation systems.

Energy-efficient Cloud Infrastructure for IoT Device Management: A Comprehensive Analysis of Edge-Cloud Workload Distribution Strategies

The rapid expansion of the Internet of Things (IoT) has led to significant increases in data traffic and computational demands on cloud infrastructures, raising concerns about energy consumption in data centers. This article explores innovative approaches to creating energy-efficient cloud infrastructures for managing IoT device fleets through optimized workload distribution between edge devices and cloud resources. It proposes implementing energy-aware algorithms that dynamically determine the optimal location for data processing based on energy consumption, latency requirements, and workload characteristics. The system employs advanced machine learning techniques for workload prediction and resource allocation, demonstrating substantial improvements in energy efficiency while maintaining high-performance standards. Case studies in smart cities, agricultural monitoring, and transportation networks validate the effectiveness of this approach in real-world scenarios. The results indicate that intelligent workload distribution across edge and cloud platforms can significantly reduce energy consumption while enhancing system performance and operational efficiency, providing a sustainable pathway for large-scale IoT deployments.

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs.

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

Creating a labelled district heating data set: From anomaly detection towards fault detection

For an efficient operation of district heating systems, being able to detect anomalies and faults at an early stage is highly desirable. Here, data-driven machine learning methods can be a cornerstone, particularly for fault detection in district heating substations, where the availability of heat meter data keeps increasing. However, the creation of data sets suitable for training such machine learning models poses challenges to researchers and practitioners alike. To address this problem, we propose a systematic and domain-specific process for data set creation for fault detection in the form of practical guidelines. This process concretizes the data science and data mining cross-industry standard CRISP-DM for the district heating domain and focuses on the process steps of goal definition, data acquisition and understanding, and data curation. We aim to enable researchers and practitioners to create data sets for fault detection in the district heating domain and therefore also enable the creation or improvement of machine learning models in this domain. In addition, we propose a minimum viable feature set for fault detection in district heating networks with the goal of enabling better cooperation between researchers and easier transfer of the resulting machine learning models, to better proliferate new progress in the field.

Data-driven robust optimization scheduling for microgrid day-ahead to intra-day operations based on renewable energy interval prediction

The uncertainty in renewable energy forecasting significantly impacts microgrid scheduling, and traditional scheduling schemes are often overly conservative and limited by a single time scale, resulting in unreasonable strategies that fail to balance reliability and cost-effectiveness. Based on this, a comprehensive two-stage day-ahead and intra-day microgrid scheduling framework is proposed integrating forecasting, regulation, and decision-making. Based on historical power data of renewable energy, a multi-kernel covariance function is applied to improve Gaussian process regression (GPR) for adaptively generating renewable energy power prediction intervals at various confidence levels and verifies the construction of robust optimization uncertainty scenario sets. In the day-ahead scheduling phase, a two-stage adaptive robust optimization model based on interval probability uncertainty sets is established to ensure minimal scheduling costs under the worst-case scenario. Meanwhile, a modified deep Q network (MDQN) algorithm based on a k-priority sampling strategy is proposed to transform the two-stage iterative process into a discrete Markov decision process for solution. In the intra-day scheduling phase, the day-ahead scheduling scheme is followed, and the intra-day scheduling scheme is optimized through multi-time-scale rolling optimization to reduce the impact of renewable energy power fluctuations. Case studies validate that the proposed scheduling method ensures robustness against uncertainties in renewable energy output while also maintaining the economic efficiency of system operations.

Optimal planning for high renewable energy integration considering demand response, uncertainties, and operational performance flexibility

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties’ impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties.

Research on collaborative reconstruction method of missing data and abnormal data in air handling units (AHU)system

The Air Handling Units (AHU), as critical components in building energy management, demand rigorous performance monitoring to ensure indoor comfort and energy efficiency. However, prolonged sensor operation often leads to malfunctions and measurement drifts due to insufficient maintenance, resulting in data gaps and anomalies. These issues pose significant challenges to system operational efficiency, occupant comfort, and energy consumption regulation. To address this dilemma, this paper innovatively leverages Bayesian inference techniques, developing a sophisticated likelihood function distance model. This model achieves precise imputation of missing data and effective rectification of anomalous data, thereby mitigating the issue of error accumulation associated with frequent reliance on historical data.Furthermore, the study exploits the intricate coupling relationships among multiple physical variables within the AHU systems to construct a comprehensive analytical model. This approach reduces the dependence on historical data for mathematical fitting and enhances the reliability of predicted data. Consequently, this paper presents an efficient methodology for handling missing and anomalous sensor data in AHU systems. Experimental results demonstrate that the relative error for reconstructing single-variable missing data remains below 7 %, while the relative error for reconstructing multi-variable missing data is even lower, at 3 %. These findings affirm the practical applicability of the methodology in calibrating anomalous sensor data within the building energy management domain.

TECHNOLOGY OF QUALITY CONTROL OF ADDITIVE MANUFACTURING PRODUCTS DURING PRINTING OF ELEMENTS OF ENERGY COMPLEXES

Additive manufacturing significantly impacts the energy sector through its ability to form complex geometries and optimize designs, enhancing the performance and reliability of energy systems and complexes. This type of manufacturing allows the creation of optimized parts considering energy efficiency and durability requirements, for example, heat exchangers can be designed and manufactured with a unique structure to improve their efficiency, and new energy devices and systems can be quickly prototyped for testing and optimization before mass production. The use of additive manufacturing can make parts production for energy complexes more localized and flexible, which is particularly beneficial when creating small and medium-sized energy systems. The paper discusses technologies for quality control of additive manufacturing products in the process of printing elements of energy complexes. Describes the state-of-the-art methods and controls used to ensure the high quality of 3D printed parts required for efficient operation of energy systems. Various aspects of inspection are covered, including printing process monitoring, flaw detection, material structure analysis, and geometric inspection. Examples are given of the use of various quality control methods in the context of the production of elements for energy complexes, making the article relevant for specialists in the field of additive manufacturing and energy. An analysis of existing printing technologies was carried out, and the advantages and disadvantages of each technology that is used in the process of serial production of additive manufacturing products for elements of power plants and complexes were highlighted. Keywords: technologies ofadditive manufacturing, additive manufacturing quality control software,artificial intelligence, energy complexes.