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Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

The electric power industry is a high-risk industry with frequent accidents. To ensure the life safety of employees and reduce the probability of accidents, it is necessary to utilize certain technical means to identify the main causative factors in electric fatal accident reports. This research proposes an accident causation identification method that combines text categorization and catastrophe association analysis. First, an improved text feature extraction method (TF-IDF-GloVe-LDA) is proposed by fusing term frequency-inverse document frequency (TF-IDF), global vectors for word representation (GloVe), and latent dirichlet allocation (LDA) algorithms. Then, an SVM classifier was used to categorize the 473 accident report texts into five categories, ACCIDENT, PROCESS, CAUSE, PROBLEM, and RESPONSE, based on the extracted text feature vectors. Second, for the 461 CAUSEs categorized from the accident report texts, the TF-IDF algorithm is utilized to mine them to obtain 62 accident causative factors. Using the word cloud map and semantic network, a visualization analysis is performed to reveal the intrinsic connection between causative factors. The human factor analysis and classification system (HFACS) is combined to construct a framework and a Boolean dataset for electric power fatal accident causative factors. Finally, the catastrophe progression method is utilized to improve the FP-growth algorithm and propose a catastrophe association analysis method. It is utilized to mine the Boolean dataset to obtain 28 association rules and construct the accident causal network. The main causative factors were ultimately identified by analyzing the critical causal chains within the network and examining the frequency and sensitivity of each node. The findings of this study indicate that a lack of effective management, supervision, and training serves as the fundamental cause of fatal accidents in the electric power industry when compared to equipment and environmental factors. This paper offered a fresh perspective on identifying primary accident causative factors in non-standardized text and exploring their intricate mechanisms of interaction, which holds significant implications for the pre-control management of accidents in the power industry. [ABSTRACT FROM AUTHOR]

This research paper aims to investigate the application of a unified power flow controller (UPFC) for relieving power congestion on a 500/230 kV grid, Power congestion occurs when the demand for electrical power exceeds the transmission capacity of the grid resulting in potential stability issues and voltage fluctuations. The UPFC is a flexible and advanced power electronic device that can regulate power flow and voltage levels in real-time hence offering a potential solution to alleviate congestion. This study explores the principles of UPFC operation its control strategies and the impact of its installation on power system stability and reliability. The research methodology involves computer simulations and analysis of the results obtained. [ABSTRACT FROM AUTHOR]

Electric power marketing system to electric power group provides the convenience, makes the power organization economic gains and also solve the many problems, but with the deepening of the application system, electric power group met with a lot of network attacks, shows that the system is under a state of risk, if attacked, the key information of power marketing may be leaked, resulting in economic and other losses. Therefore, in order to better protect the electric power marketing system, this paper has carried on the research. This paper introduces the current situation of network security protection of electric power marketing system, and then puts forward the protection technology scheme. The research shows that the technical scheme in this paper has better protection intensity and can protect the safety of electric power marketing system more effectively. [ABSTRACT FROM AUTHOR]

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heuristics algorithms are used in testing the effectiveness of TLBO, which confirm that the TLBO is indeed powerful and robust enough to give quality solutions in solving energy-trading problems. [ABSTRACT FROM AUTHOR]

In recent times, various research studies have proven that the marine currents turbine (MCT) resource has enormous potential for electric power. For the proper exploitation of this resource, it is necessary to understand the hydrodynamics of the marine current turbine. In this paper a modeling was done in order to design a horizontal axis MCT, in an optimal way. In the first step, we evaluated the energy potential of the northern region of Morocco Ksar Srir. In the second step, we described the different building blocks of the conversion chain of a marine current turbine system. Finally, we present the simulation results obtained on Matlab/Simulink. The multi-physical and the hydrodynamic modeling were tested on the P66 marine current turbine of Guinard energy of 3,5 kW. [ABSTRACT FROM AUTHOR]

The two related articles (GeoGebra Tool: Development of Applications for Electrical Machines and Drives Teaching Support and The Efficacy of GeoGebra Tool in Enhancing Electrical Machines and Drives Instruction) are parts of the unique and comprehensive research approach to improving university instruction of electrical machines and drives. The second part of this educational investigation presents the evaluative research on the applicability and quality of the described set of applications. The students of electrical power engineering assessed the quality of the developed EMD GeoGebra applications as very important and useful tools for learning and mastering related courses. Finally, the usefulness and effectiveness of these applications are summarised in the conclusion of the paper. [ABSTRACT FROM AUTHOR]

It can be considered that electric generating power from solar energy is an essential topic in the energy field. Several environmental factors affect the energy production of solar cells. Dust accumulation is one of the main factors which significantly negatively influences output energy. However, this topic is not investigated extensively, despite its significant impact, especially in arid areas such as Iraq. In this research paper, both theoretical and experimental techniques were applied to investigate the effect of accumulated dust particles on the efficiency of photovoltaic PV systems. An on-grid photovoltaic system was selected to achieve the experimental work. The results proved the negative effect of dust particles on the performance of the solar cell. Based on the obtained results, a new relationship was introduced between efficiency degradation and the amount of dust that accumulated on the surfaces of cells. This correlation is considered a necessity to find the characteristics of PV solar systems to improve their performance and efficiency. The new correlation introduced in this paper can be considered a promising prediction tool to estimate the characteristics of photovoltaic solar cells under different actual environment working conditions. The output power of the cleaned array system increased by 5-26% compared with the untreated system over the test period. Furthermore, the performance ratio (PR) was enhanced within the cleaned array system by 3 to 6 compared with the uncleaned array. A significant formula introduced the connection between the actual output power of the PV systems and the environmental condition (dust accumulation), where it can be considered as feedback to keep the performance in a steady status, which means obtaining the highest output power. [ABSTRACT FROM AUTHOR]

Transfer learning approaches in natural language processing have been explored and evolved as a potential solution for solving many problems in recent days. The current research on aspect-based summarization shows unsatisfactory accuracy and low-quality generated summaries. Additionally, the potential advantages of combining language models with parallel processing have not been explored in the existing literature. This paper aims to address the problem of aspect-based extractive text summarization using a transfer learning approach and an optimization method based on map reduce. The proposed approach utilizes transfer learning with language models to extract significant aspects from the text. Subsequently, an optimization process using map reduce is employed. This optimization framework includes an in-node mapper and reducer algorithm to generate summaries for important aspects identified by the language model. This enhances the quality of the summary, leading to improved accuracy, particularly when applied to electrical power system documents. By leveraging the strengths of natural language models and parallel data processing techniques, this model presents an opportunity to achieve better text summary generation. The performance metric used is accuracy, measured with the ROUGE tool, incorporating precision, recall and f-measure. The proposed model demonstrates a 6% improvement in scores compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Existing studies have primarily focused on the resources and technologies related to renewable energy. However, it is essential to investigate the influence of economic, social, and governmental actions on the spatiotemporal changes in renewable energy. This paper utilizes spatial autocorrelation analysis, fixed effects modeling, and Geographical and Time Weighted Regression (GTWR) to analyze the phased characteristics and spatial differences of renewable energy electricity, based on time series and spatial layout data of renewable energy power by province and power source type in China. Additionally, it incorporates relevant economic, demographic, and investment data to identify the driving factors behind the spatiotemporal transformations of photovoltaic (PV) and wind power, as well as the main forces promoting sustainable growth. The study reveals the presence of spatial agglomeration in China's renewable energy power generation. Moreover, a positive correlation is observed between investments in environmental protection (EPI), state-owned electric power (SPI), scientific and technological advancements (STI), and renewable energy electricity. Technological progress has reduced the dependence of renewable energy on the natural environment, facilitating its promotion and widespread adoption. These findings provide empirical support and policy guidance for the sustainable and rapid growth of China's renewable energy industry. [ABSTRACT FROM AUTHOR]

Nowadays, there is extensive penetration of renewable energy sources (RESs) in microgrids such as solar power stations (SPS) and wind power stations (WPS). The RESs are power electronic converter-dominated systems that have zero inertia making the microgrid to have very low inertia. Low inertia microgrid has a high rate of change of frequency (RoCoF), and the frequency response is highly volatile. To cope with this issue virtual inertia and damping are emulated into the microgrid. Virtual inertia and damping, i.e., converter with short-term energy storage device (ESD), which delivers and absorbs electrical power depending on the frequency response of microgrid and minimizes the power variation between power generation and power consumption. In this paper virtual inertia and damping are emulated based on a novel two-degree of freedom PID (2DOFPID) controller optimized with African vultures optimization algorithm (AVOA) technique. The meta-heuristic technique, AVOA, tunes the gains of the 2DOFPID controller and also the inertia and damping gain of the virtual inertia and damping control (VIADC) loop. AVOA comes out to be superior to other optimization techniques when compared in terms of convergence rate and quality. The performance of the proposed controller is compared to other conventional control methodology that has demonstrated its better performance. The dynamic response of such a proposed methodology in a microgrid model is verified in an OPAL-RT real-time environmental simulator, i.e., OP4510. • The Renewable energy sources are mostly converter-based sources which cause the microgrid to have low Inertia. • Low inertia microgrid experiences a high rate of change of frequency. • Enhance the microgrid inertia to emulate virtual inertia and damping in the microgrid. • Estimate the inertia and damping gain of the virtual synchronous generator and tune by the two degrees of freedom order controller using different metaheuristic optimization. [ABSTRACT FROM AUTHOR]

In this paper, new cycle is developed to generate simultaneously electrical and cooling power by placing a turbine between the generator and ejector in the conventional ejector-assisted absorption cooling cycle. The aim of developed cycle is to increase the exergy efficiency of cycle by adding an electrical power generation made it more environmentally friendly and reduce its dependents of fossil energy sources. The first, second laws of thermodynamic, mass and energy balance is applied for each cycle component and the constant mixing pressure ejector model is used to develop a numerical model of proposed cycle. The results depict that the augmentation of generation temperature is positively affected the work produced in the turbine contrary for cycle coefficient of performance, for every working conditions there are a certain value of generation temperature which its exergy performance of cycle achieves the maximum, the augmentation of output pressure of turbine is positively affected the cycle coefficient of performance contrary of the work produced in the turbine and the cycle exergy efficiency and the augmentation of condensation temperature is positively affected the cycle exergy efficiency and the work produced in the turbine contrary for cycle coefficient of performance and the augmentation of evaporation temperature is positively affected the cycle coefficient of performance and the cycle exergy efficiency contrary for the work produced in the turbine The results also show that the improvement of exergy efficiency of proposed cycle is 29.41% and 46% compared with the absorption cooling cycle with double and triple effect under the same operating conditions. [ABSTRACT FROM AUTHOR]

The electric power industry is related to the national livelihood, and regional economic development is closely related to the pillar industry. At present, China's economy continues to develop steadily, and the promotion of domestic industrialization has also increased the demand for electricity. In recent years, the state power industry has made great progress, in order to further adapt to the needs of domestic and foreign market competition, the State Grid has also carried out a systematic reform of its own management system. In the reform process, strengthening the construction of performance appraisal system is a very important change. According to statistics, as of June 2020, the number of employees of State Grid has reached more than 900,000. As the most basic business unit of the state grid, the team is the cell of the state grid. It is the basis for the completion of various production management work and plays an important role in the safe production and stable output of electric power enterprises. Based on the relevant indicators of the work of the team, this paper analyzes the performance of the team through the production cost performance model, and provides suggestions for the optimization of the team for power grid enterprises [ABSTRACT FROM AUTHOR]

In this paper, the hybrid photovoltaic-thermoelectric generator (PVTEG) combined dynamic voltage restorer (DVR) system is proposed for the power quality disturbances compensation in a single-phase distribution system. The stable and precise level of input voltage is essential for the smooth and trouble-free operation of the electrically sensitive loads which are connected at the utility side to avoid system malfunctions. In this context, the hybrid PV-TEG energy module combined DVR system is proposed in this paper. With the support of the hybrid energy module, the DVR will perform the power quality disturbances compensation effectively with needed voltage and/or power. In the proposed system, the PV and TEG energy sources are connected electrically in series to produce adequate voltage for the DVR operation and the fractional factor-based variable incremental conduction (FFVINC) maximum power point tracking (MPPT) control algorithm is employed to extract the possible maximum power from the PV array. The intelligent fuzzy logic controller (FLC) is chosen for implementing the MPPT control algorithm. The half-bridge voltage source inverter (VSI) circuit and in-phase voltage compensation technique are used in the DVR for better power quality disturbances compensation. The performance and usefulness of the proposed DVR system are investigated by an extensive simulation study with four different modes of operation, the study results are confirmed that the proposed system promptly identifies the power quality disturbances for compensation. Moreover, the investigation proved that the combined PV and TEG energy module can provide better energy efficiency in converting solar irradiation into electricity. [ABSTRACT FROM AUTHOR]

The paper presents some practical aspects, useful in building a photovoltaic system for supplying electricity to an isolated apiary, existing in the Reşiţa area. Considering the specific mode of operation, distributed throughout the year, of electrical machines with relatively high energy consumption in the apiary, considerations are presented for an efficient technical-economical design of the photovoltaic system for the indicated apiary. Also presented are some of the simulations carried out with the PVGIS program for the design of the photovoltaic system in the existing apiary and a concrete solution to optimally design this photovoltaic system in terms of the electrical power provided by the system, without affecting the results of the work obtained in the apiary. [ABSTRACT FROM AUTHOR]

This paper presents the technical condition of the reel of the power supply cable of a coal extraction machine, following the performance of the technical expertise. The rehabilitation to which it will be subjected will be done by executing the intervention works which will bring back in the normal operating parameters both the structural part and the functional part. The paper presents: the verification methods of the mechanism as well as the proposed technical solutions for repairing the assembly. [ABSTRACT FROM AUTHOR]

Due to air quality concerns and stricter carbon targets, surface transport electrification is quickly spreading, posing questions on the impact it will have on national and local electrical networks. This paper proposes an agent-based model to assess the per-minute weekday and weekend impact of the uptake of Electric Vehicles (EVs) over the next decade on local electrical and charging infrastructures, aimed at local decision-makers and stakeholders for transport electrification forecasting purposes. This study compares two scenarios, the first assessing the case where no restrictions are imposed on the daily charging events, and the second scenario where the peak electrical demand period between 5pm and 8.30pm is constrained for charging. Swindon Borough is selected as case study since it has one of UK's highest EV adoption rates and has ambitious aims for decarbonisation. The results show that, over time, scenario two consistently lessened the constraints imposed on the grid by lowering the weekday and weekend peak loads up to 7% and 20%, respectively, and through lowering the usage rate of the charging infrastructure by around 12%. This scenario postponed the 5pm to 8.30pm EV charging demand to later hours, resulting in delayed load waves in residential areas that, over time, took values of higher proportion of the daily peak, forecasted to match it by 2036. However, controlling the EV demand through this strategy became less effective over time, and so, constraining charging between 7am and 2.30pm is suggested for further control. To conclude, this scenario can be portrayed in reality by adding flexibility to the grid, through the use of time-of-use tariffs (TOUTs), hence, if well implemented, postponing the upgrade of the grid and the charging infrastructure, presenting savings to the network operator, charging network stakeholders and EV users. The paper thus highlights the advantages of using a model of a heterogeneous population with fine spatial and temporal detail to provide decision-support to key stakeholders in planning energy transitions. [ABSTRACT FROM AUTHOR]

Purpose: The concept of the smart grid is relatively new. The first aim of the study is to understand the behaviour of prosumers in smart grids. The other goal is to raise awareness of the management tasks and risks of smart grids by highlighting the relevant issues of some business networks (PPP projects, outsourcing, strategic alliances etc.). Methodology: Systemized literature review was used in the paper. Results: The discussed management problems of various business networks indicate that management challenges can also be expected in smart grids, so it is worThpreparing in time. Conclusion: We found a lack of empirical research about the behaviour of prosumers and believe that studying the electric power grid of the future from a management perspective, that is, examining the possible behaviours and decisions of various actors, can provide valuable and useful information for smart grid design and safe operation insurance. [ABSTRACT FROM AUTHOR]

Electricity consumption has been intensifying due to population growth, climate change, urbanization, and the growing use of electronic devices, which are increasingly non-linear loads that cause poor power quality conditions. The trend of the Internet of Things has led to the creation of devices that encourage the efficient and effective utilization of electrical power. This in turn facilitates the development of modern power distribution structures such as smart grids. Consequently, this paper presents in detail the design, construction, and validation of a three-phase IoT smart meter intended to form part of the end-user demand side of a smart grid. The compact embedded system, with a manufacturing cost below $80 USD, features a unique electronic design that enables its installation in any load center and employs a straightforward IoT structure that includes WiFi technology for Internet communication. Also, a deployed web application was developed specifically to display the smart meter measurements. Unlike other smart meters, the proposed meter not only provides the amount of active energy consumption, but total and fundamental RMS current and voltage, active, reactive, and apparent power, reactive energy, power factor, and some power quality parameters such as, line frequency, amplitude of 64 current harmonics, and total harmonic distortion. Additionally, this study shows that the prototype achieves an absolute error of less than 1% in all its measurements. Finally, real-life applications of the developed device are demonstrated in residential environments. • A smart meter embedded in a light IoT platform that sends data directly to the cloud. • Original electronic design for multiple 3-phase parameters without invasive tools. • Smart meter validated using different loads and tested in real-life environments. [ABSTRACT FROM AUTHOR]

As for the insufficient nature of the fossil fuel resources, the renewable energies as alternative fuels are imperative and highly heeded. To deliver the required electric power to the industrial and domestic consumers from DC renewable energy sources like fuel cell (FC), the power converter operates as an adjustable interface device. This paper suggests a new boost structure to provide the required voltage with wide range gain for FC power source. The proposed structure based on the boost converter and the quazi network, the so‐called SBQN, can effectively enhance the FC functionality against its high operational sensitivity to experience low current ripple and also propagate voltage and current with low stress across its semiconductors. Furthermore, the switching power losses have been decreased to make this structure more durable. A full operational analysis of the proposed SBQN and its advantages over the conventional and famous structures has been compared and explained. Furthermore, a prototype of the single‐phase converter has been constructed and tested in the laboratory. [ABSTRACT FROM AUTHOR]

This paper proposes a design for a modified distribution static compensator (DSTATCOM) with a proposed current controller for microgrid (MG) applications. The modified DSTATCOM is incorporated in a three-phase four-wire network system to deal accurately at grid connected mode with balanced, unbalanced and excursion load for both nonlinear and inductive loads. Moreover, it handles the stability issues of the MG at fault caused islanding incidents. The design criteria of the modified DSTATCOM parameters is presented and discussed. The hysteresis current controller is introduced to drive the voltage source converter (VSC) and its reference voltage signal is generated based on the instantaneous symmetrical components theory (ISCT). Matlab/Simulink software is used to verify the performance of the modified DSTATCOM with the proposed controller under different load types and loading conditions for grid connected mode and islanded mode of MG operation. Based on the simulation results, the power factor is improved and the non-linear harmonics currents are compensated therefore the harmonic distortion of the supply currents and voltage are reduced, moreover the stability operation at fault cause islanding is maintained. The results show the superiority of the proposed modification of DSTATCOM in dealing with unbalanced, distorted and bad power factor system in comparing with conventional DSTATCOM. [ABSTRACT FROM AUTHOR]

The information system of low-voltage distribution network correctly records the topological structure of distribution network, which is the premise of fine management and safe operation of electric power grid. In the topology, the most important element is the household change relationship [1]. At present, the low-voltage distribution network topology data in the information system is manually entered, so the correctness of the information can not be guaranteed. With the continuous expansion of the scale of power grid, the structure of low-voltage distribution network is complex, the amount of data of marketing measurement [2] , GIS and other information systems is increasing rapidly, each information system operates independently, and the data circulation is poor, which makes it difficult to identify the topology data, so it is urgent to carry out topology verification. The method proposed in this paper effectively solves the problem of electric data verification, and carries out corresponding experiments and demonstrations on the results, with high accuracy. [ABSTRACT FROM AUTHOR]

Presents information on online articles offered by the web site of the "Solutions!" magazine on topics relevant to the paper industry. Use of variable frequency drives for unstable power conditions; Vector control introduction by PumpSmart Control Solutions; Motivation and sales advice by Michael Mercer.

The growth of demand, the need for economic efficiency and optimal utilisation of electric power networks and the high cost of construction of new power networks result in inevitable challenges, such as overloading and excessive power transfer along transmission lines, high losses, voltage instability, low power quality, reliability problems and voltage profile problems. To manage the power transmission system, the recently developed Flexible AC Transmission System (FACTS) can be used for electric transmission networks since it plays an important role in enhancing the static and dynamic performance of power systems. However, location, type and capacity of FACTS devices should be optimised to maximise the resulting benefits. In this paper, different types of FACTS devices are discussed along with their modelling and functions. In addition, the proposed and the compared techniques and approaches in the existing research works, such as analytic approaches, arithmetic programming methods, meta-heuristic optimisation techniques and hybrid methods, are discussed. Analytic approaches have insufficient computation accuracy in determining optimal allocation of FACTS devices and arithmetic programming approaches are often inefficient in managing constrained optimisation problems. However, meta-heuristic approaches are stochastic, population-based optimisation algorithms that are highly efficient in dealing with a multimodal, highly constrained, multi-objective and discrete system. Meta-heuristic techniques are the most commonly used methodologies to determine the optimal allocation of FACTS devices. Furthermore, the utilisation of analytic methods or classical optimisation approaches with meta-heuristic optimisation techniques plays an important role in reducing the search space of the proposed meta-heuristic optimisation technique. In the present paper, an overall review of 50 recent research work studies, including proposed and compared approaches and techniques, objective functions, approaches, the utilised FACTS devices, constraints, contingency conditions and all the analysed and simulated parameters, is provided and discussed in details. In addition, a more weighted discussion of the proposed methods based on meta-heuristic optimisation techniques is provided. [ABSTRACT FROM AUTHOR]

• Radiological overflows are productive for scientific framing projects. • The transnational nuclear assemblage emerged alongside omnipresent overflows. • The 100 mSv assumption is a technopolitical tool developed by this assemblage. • This tool firmly and flexibly frames low-dose radiation exposures as 'uncertain.' • This tool participates in marginalizing both democratic and scientific debate. This paper explores the productive role of radiological overflows in the historical evolution of the 100 mSv assumption—a technopolitical tool used to frame human–radionuclide relationality in the aftermath of Tokyo Electric Power Company's (TEPCO's) Fukushima Daiichi Nuclear Power Plant disaster in 2011. Pairing Callon's (1998: 264) "logic of framing/overflowing" with sensibilities from the field of feminist material semiotics, the analysis illuminates how a desire to contain public unrest incited by radiological overflows while simultaneously advancing nuclear weapons and nuclear energy programs has led to the formation of the transnational nuclear assemblage—a vast assemblage of scientists, institutional actors, texts, technopolitical tools, etc.—which translates radiological overflows into dominant scientific frameworks for quantifying and regulating human–radionuclide relationality. Exploring the technopolitical nature of the transnational nuclear assemblage's 100 mSv assumption, the analysis highlights how this assumption participates in enacting a firm, yet flexible frame for containing the messy heterogeneity of 'low-level'—under 100mSv—human–radionuclide relationality, as well as in marginalizing democratic and scientific debate. The paper ends with a reflection on the contingency of the transnational nuclear assemblage's framing project, and on other opportunities for attending to the complex socio-materiality of human–radionuclide relationality, particularly at low-levels of exposure. [ABSTRACT FROM AUTHOR]

The free-piston linear generator (FPLG) with a dual-piston air-driven system is a novel type of energy conversion device that converts mechanical energy into electric power through a linear generator. This paper investigates a novel FPLG utilising different generator stator core configurations (GA, GB, GC, GD, and GD) with an optimised valve opening control and the inlet pressure of 5 bar and 8 bar. The effect of generator configuration on the translator motion characteristics, root mean square (RMS) power output, as well as the system stability and the conversion efficiency, are duly discussed. The results show that the piston motion is affected by the generator configuration and the inlet pressure. The maximum RMS power achieved is 203.9 W when GB is used, whereas the maximum operating frequency of 13.8 Hz and a peak power output of 396 W are achieved when GD is used. The FPLG stability within the range for all generator configurations except for GA when the inlet pressure is 5 bar. The FPLG system conversion efficiency (thermal to work and work to electrical) is duly discussed. [ABSTRACT FROM AUTHOR]

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The low inertia of electrical power networks as renewable energy penetration levels increase is regarded as a significant issue confronting worldwide ambitions for the anticipated renewable energy transition. Load frequency control (LFC) is an efficient way of improving the frequency stability and dependability of renewable energy-based power networks. Therefore, this paper introduces a novel three-degree-of-freedom (3 DoF) load frequency control method for two-area-linked power systems. To implement the suggested LFC approach, the proposed control employs an enhanced hybrid fractional order control method (namely FOTPID), and the tilt integrator differentiator with filter (TIDF) is employed in the frequency deviation and tie-line power loop feedforward compensation. As a consequence, this study proposes a novel 3DoF cascaded FOTPID-TIDF controller. Furthermore, this work proposes a modified method of the recently developed Manta-Ray Foraging Optimizer (MRFO) to optimize the parameters of the new suggested 3DoF cascaded FOTPID-TIDF controller. The paper includes extensive performance comparisons and discussions to validate the superior performance metrics of the new proposed control and optimization methods. The results reveal that by taking into account the characteristics and uncertainties of renewable sources, interconnected loads, and grid inertia, the results perfectly mitigate various existing deviations in frequency and tie-line power compared to existing contemporary LFC approaches. [ABSTRACT FROM AUTHOR]

This paper investigates simultaneous lightwave and information transfer (SLIPT) based non-orthogonal multiple access (NOMA) in underwater optical wireless communication (UWOC) systems. At the user signal separation (SS) based SLIPT scheme is employed. Imperfect successive interference cancellation (SIC) at the near user is considered. Firstly, we derive the maximum allowable electrical power for message symbols at the transmitter, ensuring input to the laser diode lies within the linear region and. A novel linearity-constrained transmitter design is proposed for the uniformly distributed message, followed by calculations of instantaneous data rate and instantaneous harvested energy for both users. Then, closed-form expressions for average data rates (AR) and average energy harvested (AE) under weak turbulence-induced fading channels using Holtzman's approximation are derived. The tightness of the approximation is confirmed by Monte-Carlo simulations. AE–AR boundaries for both users show an initial rise and subsequent decline in data rates with increased harvested energy, owing to higher bias values restricting transmitter power. The study also examines trade-offs between AE and AR across various system parameters including beam divergence angles, imperfection of SIC, power allocation coefficients, and water types, is conducted. We observe shifting and shrinking/expansion of the AE–AR region depending upon the variation of the various system parameters. We also show that the discussed SS scheme is general and encompasses time-switching (TS), and power-splitting (PS) schemes as special cases. We also briefly discuss as to how the developed framework could be extended to more than two users. In summary, the overall effective energy consumption deduces owing to the adoption of SLIPT at the users, making the considered setup more energy-efficient. [ABSTRACT FROM AUTHOR]

With the volatile oil prices and the need to reduce the emission of greenhouse gases, renewable energy resources are a very attractive alternative. Africa has a good potential for micro hydroelectric power generation. However, only a small proportion of this potential has been harnessed. As a result, most rural areas in Africa have limited access to electric power. Unfortunately, those rural communities privileged to be connected to the grid also have unreliable and expensive grid power. To meet their energy needs, some of the communities have installed and operate Pico or Micro Hydro Power Plants (MHPPs) based on the Build-Own-Operate (BOO) model. In any power generation plant, a speed governor is required for regulating the frequency of supply under varying load conditions. However, this is the single most expensive component in a MHPP and most of the community owned plants can hardly afford it and thus, have no means of continuously regulating frequency. An Electronic Load Controller (ELC) is a device used to control the sharing of constant generator output power between the consumer and dummy loads and hence, maintain the frequency of a MHPP constant. This replaces the expensive and complex speed governor. In this paper, the design and implementation of an ELC is presented. Magnetostrictive Amorphous Wire (MAW) is used as a frequency sensor. An amplifier and a signal conditioning circuit were designed to convert the analog signal from the MAW to a digital signal that is fed to microcontroller. In addition, optimal Proportional-Integral (PI) gains are determined to serve as reference gains to the microcontroller so as to generate a Pulse Width Modulation (PWM) signal to control the firing angle of the switching circuit of the ELC. The laboratory experimental test results obtained clearly show the effectiveness of the ELC to dump excess power to the damper load when the consumer load changes, and maintain the supply frequency between 49.5 and 50.5 Hz. Also, from the results, it was observed that the MAW sensor was able to measure the frequency effectively with high accuracy when compared to the frequency measurement from the tachometer. This is a key finding of the suitability of MAW sensor application in ELC for MHPPs. The designed controller is cheaper and yet able to control the frequency very effectively when compared to other ELCs and speed governors that are currently on the market. • The measured frequency using the MAW sensor compares well with that obtained using a digital tachometer: This is a key finding or novelty in this paper since the application of MAW sensor for frequency measurement in ELCs for MHPPs has not been reported in literature. • The designed ELC is effective to dump excess power to the damper load when the consumer load is less than the generator capacity. • The stabilising time for the designed ELC (obtained by using a timer) is 1.5 seconds. • The designed ELC is cheaper when compared to other ELCs and speed governors that are currently on the market. [ABSTRACT FROM AUTHOR]

Abstract The restructuration of electric power sector has renovated the power system operational planning. In the deregulated market, electricity is treated as an entity unlike in the traditional vertically integrated market model where it is treated as a service provided by the generation companies (GENCOs). The task of GENCO is to perform self-scheduling of available units so as to achieve maximum profit in restructured power sector. Therefore, the problem of profit based unit commitment (PBUC) in deregulated markets can be formulated as a commitment and generation allocation through self-scheduling procedure. The commitment schedule optimization, i.e whether the status of a thermal unit is to be changed to on or off state, is a binary problem. Thus, the PBUC problem requires binary optimization for commitment and real valued optimization for generation allocation. In this paper, three binary grey wolf optimizer (BGWO) models are presented to solve the profit based self-scheduling problem of GENCO. The BGWO models proposed in this paper differ with respect to the transformation function used to map real valued wolf position into a binary variable. The binary mapping of commitment status is carried out using sigmoid and tangent hyperbolic transfer functions. Also, in the sigmoidal transfer function, two binary transformation procedures, namely crossover and conventional sigmoidal transfer function, are presented. The effectiveness of the proposed models in improving the solution quality of PBUC problem is examined using two test systems, a 3 unit and a 10 unit test system. In addition, two cases of GENCO market participation with and without reserve market participation are simulated. In the test case with reserve market participation, two commonly used reserve payment models are examined. Simulation results are presented and compared to existing approaches that have been used to solve the PBUC problem. The simulation results and statistical analysis demonstrate the improved solution quality (profit or fitness value) of the PBUC problem and statistical significance of the BGWO models with respect to solution quality obtained as compared to other established meta-heuristic approaches. [ABSTRACT FROM AUTHOR]

Abstract In order to assess how seasonality affects disparities in natural gas consumption among sectors, this paper aims to study the pattern of convergence in natural gas consumption in a sample of 11 sectors in the United States between January 1973 and February 2017. In addition to the full sample, the existence of convergence is also examined in five subsets of sectors: residential, commercial, industrial, transport and electric power. By using various types of unit root tests, empirical results provide significant support for the convergence of disaggregated natural gas consumption across sectors in the United States. Another important finding of this paper is that natural gas consumption, despite being convergent, is very persistent. Highlights • This study is to test the hypothesis of the convergence in disaggregated natural gas consumption for the United States. • We use various types of unit root tests. • The energy conservation policies oriented towards the reduction of the seasonal effects of natural gas consumption. [ABSTRACT FROM AUTHOR]

Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard train operating conditions for simulation, we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running. Compared with traditional predictive control systems, energy efficiency 10.5% higher. This system provides good stability and robustness, satisfactory speed tracking performance and control comfort, and significant suppression of disturbances, making it feasible for practical applications. [ABSTRACT FROM AUTHOR]

The multi-objective optimization algorithms (MOO) are used to obtain the best compromising solutions when two or more objective functions need to be optimized simultaneously. The convergence and diversity are critical factors to consider while solving the MOO problems because they determine the possibility of obtaining an evenly distributed Pareto front. This paper proposes a hybrid optimization algorithm to solve a multi-objective economic emission dispatch (MOEED) problem of electrical power systems. The electrical power generated by consuming fossil fuels is very costly and also burning of these fuels contributes to global warming. Hence, electrical power is generated at the least cost and emission. The MOEED problems have been solved in the past by using swarm intelligence and evolutionary algorithms. However, the solutions reported in the literature, are either inferior or the constraints are violated. The algorithm proposed in this paper is an integration of an Artificial Bee Colony optimization algorithm and a Non-Dominated Sorting Genetic Algorithm-II. The effectiveness of the proposed algorithm is evaluated by applying it to three test systems having 6, 10, and 40 coal-based generators. Additionally, various multi-criteria decision-making algorithms are used to identify the best non-dominated solutions obtained by the proposed algorithm and compared with previously reported results. The best fuel costs obtained by the proposed approach, for a 6-unit test system with and without transmission losses are found to be 605.9983 and 600.11140 $/h respectively. While the best emission values, for this test system, with and without transmission losses are found to be 0.1941 and 0.1942 tons/h. Moreover, the best fuel costs obtained by the proposed approach, for 10 and 40-unit test systems are found to be 111181.9871 and 121369.0838 $/h respectively. Furthermore, the best emission values for these test systems are found to be 3932.24322 and 176682.264 tons/h respectively. All these results are obtained without constraint violations and within 10–600 iterations. • Combined economic emission dispatch problem is solved by ABC-NSGA-II Algorithm. • Best compromising solution, best fuel cost, and best emission values are compared. • Results of combined economic emission dispatch problem in old articles are analyzed. • Multi-criteria decision-making methods are used to find trade-off solution. • Statistical analysis using hypervolume indicator is presented. [ABSTRACT FROM AUTHOR]

The distribution of power flow can be effectively improved by optimizing the configuration of Distributed Generators (DGs) and Shunt Capacitors (SCs). Proper placements and capacities of DGs or SCs for real power loss minimization at the radial distribution network (RDN) is attracting more attention as people become more and more dependent on electric power resources. In order to overcome the slow convergence and poor effect of intelligent algorithms in this problem, Particle Swarm Optimization with Orientation and Shrinking factor (PSO-OS) is proposed in this paper. In the suggested PSO-OS algorithm, sensitivity analysis sort of distribution network is introduced to guide the direction of variation to simplify the search space, and shrinking factor is introduced to balance search capabilities in various RDN systems. The proposed method is applied to IEEE-33, IEEE-69 and IEEE-119 systems to solve the problem of simultaneous allocation of DGs and SCs. Simulation results show that the energy loss of the system is reduced and the stability is improved after reconfiguration. By repeating the experiment independently with the traditional PSO algorithm, the comparative analysis of the results proves the superior performance of the improved algorithm. By comparing with the results in recent literature, it reveals the effectiveness of the proposed PSO-OS algorithm in finding the optimal position and capacity of DGs and SCs in the distribution network. [ABSTRACT FROM AUTHOR]

This paper introduces a new HT-type module for both symmetrical and asymmetrical multilevel inverters (MLIs) with reduced power electric components. The proposed module comprises of two back to back T-type inverters connected with four cross-connected switches. This MLI addresses two major drawbacks associated with the conventional and other recently proposed MLIs which are the high voltage stress of switches and higher power component counts. The structure of the proposed device is designed combining the beneficial features of both T-type inverter and cross-switched inverter. The 1st feature allows the module to produce negative voltage levels deprived of any extra H-bridge circuit and thus decreasing the overall voltage stress significantly. The latter feature allows the device to utilize reduced number of switches to produce higher voltage levels. A comprehensive comparative analysis is additionally established between other MLI topologies to confirm the superiority of the HT-type MLI. From this analysis, it is found that the proposed MLI has successfully overcome the issues associated with other MLIs, it was able to produce the results following industrial standards and it outperformed other MLIs in different aspects such as power components count, cost, efficiency and dynamic performance. Selective harmonic elimination pulse width modulation (SHE-PWM) and nearest level control (NLC) algorithm is considered in this manuscript to generate the output results from the proposed HT-type module. All the details about the operating principle and modulation techniques utilized for the proposed MLI module are stated and validated by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Estimating the parameters of a Photovoltaic (PV) cell is crucial, given the significant integration of the PV systems into electrical power systems. One of the primary challenges in the estimation of PV cell parameters is identifying a generalized method applicable to any PV system, irrespective of environmental variations and power ratings. This paper introduces a novel application of an optimized deep neural network designed to estimate the parameters of the PV systems across a range of temperatures, irradiance values, and PV module ratings. The network undergoes a training process by utilizing data obtained from the PV module block located within the Simulink library. In order to evaluate the effectiveness of the proposed methodology, the network is subjected to a series of assessments. These assessments encompass the utilization of PV cell data from the Simulink library, comparisons with recently developed methods, and practical evaluations using experimental PV cell data to estimate the PV cell parameters. The findings underscore the simplicity and precision of the proposed method across diverse PV cells. • PV parameter estimation. • Optimization. • Deep learning. [ABSTRACT FROM AUTHOR]

This paper deals with a non-interactive approach to solve multi-objective thermal power load dispatch (MTPLD), where either decision maker is not involved or preference information is available in prior. To reduce the computational complexities due to generation of Pareto-front and selection of satisficing solution, this paper adopts no-preference approach. A satisficing function to resolve the conflict of non-commensurable objectives is proposed, which reformulates the MTPLD problem as scalar thermal power load dispatch (MTPLD) problem. Owing to ambiguous or vague in objectives, the proposed method exploits fuzzy theory. MTPLD problems’ satisfying solution is obtained by implementing hybrid chaotic differential evolution algorithm and Powell’s pattern search algorithm (CDEPS). The chaotic differential evolution algorithm is responsible for the diversification of feasible solutions and provides global solution. Whereas Powell’s pattern search, method improves the exploitation by performing local search. The paper investigates the performance of two CDEPS variants based on Gauss map and Tent map, respectively. The performance of the proposed solution procedure is analyzed using generalized benchmark test functions and complex MTPLD problems. The exhaustive analysis using non-parametric significance test and descriptive statistics shows that the Tent map based CDEPS solution procedure has better ability to generate quality generation schedule and faster convergence rate. [ABSTRACT FROM AUTHOR]

In this paper, indices that could evaluate electrical energy poverty among micro enterprises are developed with the aim of pointing out and suggesting the area of intervention to improve productivity of the micro enterprises. These indices are: device suitability index, electricity affordability index, electricity availability index and composite electrical energy poverty index. The indices are measures in a scale of 0–1 with 0 being the least electrical energy poverty (desired value) while 1 denotes ‘worst’ acute electrical energy poverty. To enhance the understanding and applicability of the developed indices in measuring electrical energy poverty, data relating to electricity usage among micro enterprises for the city of Ibadan is obtained through questionnaire and interview of six (6) most common groups of micro enterprises (barber salon, beauty salon, document &IT, tailoring, sales consumables and sales non-consumables (i.e. clothes, shoes, electronics etc.) in sixteen (16) different locations in the city of Ibadan. The data are then applied to the developed indices in a step wisely manner. Some of the key results reveal that micro-enterprises in the city of Ibadan are doing well in terms of possession of electrical device. However, electricity availability is poor with significant negative effect on business productivity. The developed indices have the capability to suggest the areas that need improvement in order to improve overall electrical energy poverty. This paper serves as a good tool that could aids decision making at improving the business activities of micro enterprises. [ABSTRACT FROM AUTHOR]

Faster RCNN is a popular and very efficient object detection algorithm, it has good performance in image recognition in various fields. In this paper, we applied the Faster RCNN algorithm to the equipment recognition and status detection tasks in the electric power room, then proposed a complete technical implementation scheme. First of all, after site investigation and shooting, we collected a total of 5600 pictures in 100 categories of electrical equipment. Then, we achieved the labeled pictures for all categories by employing an open source software named Labelimg. In the next step, the annotated data was randomly shuffled, 70% of which was used as the training set for model training, 10% of which was used as the validation set for model tuning, and the remaining data was used as the test set for model testing. We get less than 0.01% loss in training step and get 91.3% mAP for all test pictures in testing step. Besides, the test for video received a great performance. The well performance on the test pictures and videos showed the effectiveness and implementability of our technical solutions. [ABSTRACT FROM AUTHOR]

This paper provides an application of the hybrid Grey Wolf Optimization and Particle Swarm Optimization (GWO-PSO) method to reach a solution to the optimal reactive power dispatch (ORPD) problem in the scope of electric power networks. PSO is a swarm based meta -heuristic optimization algorithm whose target is to seek the best solution to a problem by moving particles in a specific exploration field. On the other hand, GWO is a meta -heuristic optimization technique which is inspired by grey wolves. In this article, GWO is hybridized with a PSO method to improve the progress of the GWO. There are two objectives minimized in this research study to improve the electric power network performance. They are: 1) power losses in the transmission systems, and 2) the deviation of voltages at the load buses. The problem of ORPD has many restrictions on the networks which must be considered during the solution. The hybrid GWO-PSO is proven as an effective optimization technique when seeking the global best solution to an optimization problem. The success of the introduced hybrid technique is verified utilizing more than one standard IEEE test system. A valuation to the introduced technique is performed by comparing it with other optimization techniques stated through the literature. The simulation results confirm that the usage of the hybrid GWO-PSO techniques causes an observable improvement in a wide scale of the electric power networks behavior. [ABSTRACT FROM AUTHOR]

Present paper describes a solution regarding automatic optimized functioning of hydraulic turbine used at a micro hydropower plant, with maintaining on the upstream of a prescribed water level. The solution, implemented in specific Ladder Logic software, proposes a stable control system through the oscillations of the water level in the surge tank, which can be adjusted for any type of micro hydropower plant. The results of the tests, performed for different variations of the stream flow, confirmed a stable functioning of the control system for water level fluctuations of ± 5 cm, under normal operating conditions. The proposed method was implemented and tested for a horizontal Francis turbine, with a power of 250 kW, connected to a current power supply. A practical example illustrates the application of this type of control system solution. [ABSTRACT FROM AUTHOR]

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This paper presents an efficient procedure for optimizing the electric field at ground level of high surge impedance loading transmission lines. Such lines have the capacity to achieve higher power transmission rates than the conventional ones. The ellipsoidal method is applied in order to maximize the transmitted power and minimize the electric field at ground level through the variation of the conductor's positions in the tower, given its physical and electrical constraints. By means of an efficient new approach to handle the conductors, the optimization gives birth to compact line designs combined with conventional bundles of conductors. Furthermore, it is shown that the proposed strategy can increase the transmitted power, reduce the electric field at ground level, and shorten the running time of the optimization algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the accumulation of technological capability in latecomer suppliers of complex capital goods by exploring the issue in an Iranian supplier of large hydro electricity generation systems. The findings suggest that, contrary to the conventional paths of catch-up in Asian and Latin American mass-producing electronics firms, and instead of starting from early stages of product lifecycle, the technological capabilities in this case were acquired through a non-linear movement within the stages of designing and installing complex capital goods in projects: starting from the middle stage (engineering and realisation of complex goods in projects), moving to the last stage (operation and troubleshooting of complex goods), and eventually coming back to the first stage (conception and design of complex goods). A detailed analysis of learning mechanisms reveals that this path emerged largely due to the nature of “learning by projects” in this industry, engineering intensity of the tasks, and insufficiency of conventional knowledge transfer methods for building design capabilities in complex systems. The paper starts by developing an initial framework of technological capabilities that is then refined through this study and the resulting framework is presented as one of the contributions. Based on the findings, a set of new empirical categories of technological capabilities in complex capital goods is suggested to help future research in this area. Implications of this research are also discussed for global value chains literature. The conclusion touches upon the implications of the study for business strategy and policy. [ABSTRACT FROM AUTHOR]

Electrical power sources used in outer planet missions are a key enabling technology for data acquisition and communications. State–of-the-art power sources generate electricity from alpha decay of 238 Pu via thermoelectric conversion. However, production of 238 Pu requires specialist facilities including a nuclear reactor, a source of 237 Np for target irradiation and hotcells to chemically separate neptunium and plutonium within the irradiated targets. These specialist facilities are expensive to build and operate, so naturally, a more economical alternative is attractive to the space industry. Within Europe 241 Am is considered a promising alternative heat source for radioisotope thermoelectric generators (RTGs) and radioisotope heating units (RHUs) since, as a daughter product of 241 Pu decay, 241 Am exists in 1000 kgs quantities within the UK civil plutonium stockpile. A chemical separation process is required to extract the 241 Am in a pure form and this paper describes the AMPPEX process (Americium and Plutonium Purification by Extraction), successfully developed over the past five years to isolate 241 Am in high yield (> 99%) and to a high purity (> 99%). The process starts by dissolving plutonium dioxide in nitric acid with the aid of a silver(II) catalyst, which is generated electrochemically. The solution is then conditioned and fed to a PUREX type solvent extraction process, where the plutonium is separated from the americium and silver. The plutonium is converted back to plutonium dioxide and the americium is fed forward to a second solvent extraction step. Here the americium is selectively extracted leaving the silver in the aqueous phase. The americium is stripped from the solvent and recovered from solution as americium oxalate, which is calcined to give americium dioxide as the final product. This paper describes the development of the AMPPEX process over a series of six solvent extraction separation trials using centrifugal contactors. The technical underpinning reported here has allowed the design of a facility capable of producing 25-50 g of americium per day. [ABSTRACT FROM AUTHOR]

In this paper modelling and analysis of stand-alone dual stator induction generator (DSIG) with non-identical parameters under a new method has been done. The induction machine has two sets of stator three-phase windings spatially shifted by 30 electrical degrees. One supplied power to the DC load via a rectifier bridge and AC capacitors, and the other was connected to the battery banks by an inverter to store energy and restore it to regulate the power of the first stator in weak wind. We develop the steady state model and show the performances of different configurations by the simulation results. [ABSTRACT FROM AUTHOR]

This paper describes and analyzes a system that tightly couples an electrochemical oxidation cell (EOC) with a protonic-conducting ceramic separation cell (PSC) to produce compressed pure hydrogen from a hydrocarbon feedstock and water. The EOC introduces oxygen through its membrane-electrode assembly (MEA) into the fuel chamber, enabling partial oxidation and reformation of the fuel to produce a hydrogen-rich mixture within the anode microstructure. At the same time, the EOC generates the electric power needed to drive the PSC for hydrogen separation and compression. On the other hand, removing hydrogen from the fuel stream by PSC can thermodynamically promote the catalytic conversion of the fuel stream. The concept leads to a self-contained integrated system, being independent of any external electrical power source, and being capable of producing pressurized hydrogen with potentially high energy-conversion efficiency. [ABSTRACT FROM AUTHOR]