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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]

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]

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]

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]

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]

Compressors can be considered as one of the most important utilities in industrial processes. Around 10–14 percent of the electricity consumption in industry was used to produce compressed air. So, in industrial facilities, compressors are the largest energy-consuming equipment. Therefore, energy saving in multi-compressor systems (MCS) is very challenging. In this paper, a new symbolic model based on multi objective Jaya algorithm for the MCS is proposed. The proposed model considered an optimal electrical power consumption (EPC) as well as optimal life span while maintaining the demanded compressed air flow during load change. The proposed model selected compressors that would meet the demanded air flow by adjusting their partial load ratio (PLR) for each changeable variable speed drive compressor (VSDC). This study has optimized the operating period for each compressor based on the optimal power consumption of MCS as well as optimal life span of each compressor. Evolutionary multi-objective Jaya algorithm is proposed to solve the optimization problem. Furthermore, Grey Wolf optimizer has been used and its results have been compared to show that MO-Jaya showed better performance with the proposed problem. Different practical cases of study have been considered and analyzed showing that the multi-objective optimization model could dramatically reduce consumption costs and increase machine's life span. Besides, the maintenance schedule of each compressor could be predicted and planned based on the MCS schedule. Variant air flow has been applied to test the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Chinese electric power sector (EPS) contributes 52% of China's total CO 2 emissions. Given the severe global warming situation, carbon information disclosed by Chinese electric power enterprises (EPEs) has received extensive attention. Therefore, to understand the current level of carbon information disclosure (CID) in EPS and improve the CID quality, this paper evaluates the CID quality of public companies in the EPS. Based on the characteristics of the EPEs, this paper established a CID quality evaluation system from five aspects: integrity, relevance, reliability, comprehensibility, and comparability. An ANP-Cloud model is employed to evaluate the CID quality of 62 public companies in China's EPS. The analytic network process (ANP) method can quantify the correlation among evaluation indexes. The Cloud model achieves the transformation from qualitative evaluation to quantitative scoring with consideration of the uncertainty and fuzziness of the expert's evaluation language. The integration of two models, therefore, improves the comprehensiveness and objectiveness of the evaluation results. The evaluation results indicate that: (1) "Carbon emissions", "Carbon emission reduction targets" and "Independent chapter" indexes disclosed by the EPEs have a significant impact on CID quality. (2) The CID quality of the entire EPS public companies is low. The carbon information disclosed by EPEs cannot effectively represent the carbon management behavior of enterprises. (3) From the internal perspective of the EPS, the CID quality of thermal enterprises is relatively high, while hydropower enterprises are relatively low. Finally, this paper proposes viable suggestions to improve the CID quality from three aspects: EPEs, government, and public monitoring. • Constructed a carbon information disclosure (CID) quality evaluation system for electric power sector. • Integrated analytic network process (ANP) method and Cloud model to make the evaluation result reasonable. • Evaluation results show that the overall disclosure quality is low. • Indexes concerned by the carbon information users are determined. [ABSTRACT FROM AUTHOR]

La Prospective is an appropriate approach for contexts distinguished by high uncertainty and social conflict. This French scenario building method has received limited attention in the field of energy resources planning, especially beyond approaches focused on technological issues. The paper utilized the La Prospective method to generate strategic 2050 visions, integrating energy resources and spatial development. The Araucania region in Chile served as a case study, engaging 52 stakeholders, both experts and members of the public. The method was implemented in three stages: (a) system assessment; (b) understanding the dynamics of the system; and (c) obtaining strategic future visions. The results show a regional vision based on key concepts: (i) affordable electric power that protects the environment and clean air; (ii) preference for individual self-generation and/or collective systems (iii) digitally connected and available to everyone. The paper closes with a discussion on how participatory processes enable the elaboration of strategic futures based on high levels of consensus among stakeholders with different interests and professional backgrounds and provides a few recommendations for future research. • It generates long-term strategic visions on energy resources and spatial development. • It uses a participatory approach involving a total of 52 stakeholders during the process. • Water resources, the native population´s worldview, and landscape quality are seen as crucial for future scenarios. [ABSTRACT FROM AUTHOR]

In this paper, the DC fast charger module with its circuit topologies is studied detailed. The electric vehicle batteries are charged via DC fast chargers for quick charging. The electrical power goes through AC to DC and DC-DC power converters and reaches the battery. The first stage's responsibility is converting AC to DC, drawing low harmonic content and low reactive power from the grid, and providing fixed DC bus voltage. However, the latter task is charging the battery with a controlled current or voltage level. The common topology in the AC-DC converter is the Neutral Boost PFC in Vienna Rectifiers. However, Phase-shifted Full Bridge or LLC Resonant Converters are popular on the DC-DC side. The circuit models for those power stages are modeled in the PC platform and obtained the system efficiency of the DC Fast Charger. In the last part, the proposed idea is presented which involves sharing the charge of the electric vehicle battery between two power converters. The proposed study results show that the DC fast charging system efficiency increases remarkably on high output voltage levels. [ABSTRACT FROM AUTHOR]

Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations' efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, including wind power. The Random Vector Functional Link (RVFL) is an efficient ANN model that can be employed in time-series forecasting applications. However, the configuration process of the RVFL needs to be improved. Thus, in this paper, we presented an optimized RVFL network using a new naturally inspired technique called the Capuchin search algorithm (CapSA). The main function of the CapSA is to boost the configuration of the traditional RVFL and enhance its prediction capability. We implement extensive evaluation experiments using public datasets from four wind turbines located in France, using several evaluation measures called RMSE, MAE, MAPE, and R2. The evaluation outcomes reveal that the CapSA-RVFL obtained the best prediction accuracy compared to the original RVFL and several variants of the RVFL model, which verifies that the application of CapSA has a significant contribution to improving the prediction capability of the RVFL. [ABSTRACT FROM AUTHOR]

The increasing demands of electrical power have imposed a necessity to integrate renewable energy power sources (REPS) into electrical power networks. The presence of REPS within electrical distribution networks mandated an update in the settings of the overcurrent protection relays. In this paper, a real-time adaptive protection scheme is proposed to redefine the settings of the overcurrent protection relays by replacing the traditional time multiplier setting (TMS) with a newly proposed real-time adaptive time multiplier setting (ATMS). The proposed ATMS is formulated as the product of two factors namely; the acceleration factor (AF) and the tuning factor (TF). The AF is set as the ratio of the impedance seen by the relay during a fault to the pre-fault impedance. The TF is a multiplication constant that is determined offline prior to operation in order to optimize ATMS. The scheme was tested upon different radial and meshed bench-mark testing systems; the IEEE 34 system and the IEEE 30 bus system employing MATLAB software package. The proposed scheme proved its capability of optimizing the response of overcurrent relays and minimizing total operating time. In addition to its adaptability to different operational and loading conditions during the day. [ABSTRACT FROM AUTHOR]

For the purpose of improving the resources utilization efficiency and decrease network redundancy of electric power communication network (EPCN), the paper explores using failure independent path protection preconfigured cycle (FIPP p-Cycle) to provide backup paths for electric communication service and presents a protection algorithm of enhanced FIPP p-Cycle with traffic grooming (EFIPP-TG). Firstly, the multiplexing conditions of FIPP p-Cycle are analyzed specifically, and we propose a FIPP p-Cycle multiplexing algorithm to improve the utilization efficiency of backup resources. Then segment protection algorithm for working path is designed to decrease service blocking ratio as much as possible. In addition, by taking advantage of traffic grooming strategy, the routing paths of services are optimized, yielding effective decrease of network blocking probability. Simulation results from different topologies demonstrate that compared with traditional FIPP p-Cycle scheme, other p-Cycle based protection algorithms and shared backup path protection algorithm, the proposed EFIPP-TG algorithm can significantly reduce the redundancy and blocking probability of networks, shorten the length of backup path and hence improve the reliability of service and performance of network survivability. [ABSTRACT FROM AUTHOR]

The electric power transformer is an essential component of an electrical power system because it regulates the voltage level to provide the best possible system operation. Thus, it is important to detect the transformer faults to maintain its reliability as high as possible. The sweep frequency response analysis (SFRA) test is one of the tests which, used to diagnose transformer faults. It determines the fault type by analyzing its response across a wide frequency range. The main advantage of the SFRA test is the detection of mechanical deformation faults since mechanical deformation faults cause the high-frequency equivalent circuit parameters to change which, could be detected easily using the SFRA test. Also, statistical features are defined for this SFRA response used in fault detection and classification. The transformer high-frequency equivalent circuit parameters are non-uniform distributed over discs due to normal leakages and aging. Thus, in the present paper, the non-uniform distribution of the transformer high-frequency equivalent circuit is studied in the SFRA test at different faults and various non-uniformity levels. Finally, when considering the non-uniform distribution of high-frequency equivalent circuit parameters, the results demonstrate that some features are greatly affected, and others are just minimally affected. Depending on how much each feature is affected by the uniformity of high-frequency equivalent circuit parameters, rules of fault diagnosis algorithms based on the SFRA test could be reweighted to improve their accuracy. Also, the obtained results could be compared to real measured data to estimate the actual non-uniformities of transformer parameters. [ABSTRACT FROM AUTHOR]

Mathematical design of a new fourth-order harmonic passive filter is presented and its optimal strategy is proposed with regards to various design scenarios. The considered objective function is total demand distortion minimization of the supply current. The crow spiral-based search algorithm (CSSA) is applied to solve the design problem formulated. This CSSA relies on a spiral search mechanism that improves the convergence and balances the exploration and exploitation abilities of the conventional crow search algorithm. Various power quality indices are taken into consideration like the individual and total harmonic voltage distortion values, individual and total current harmonic distortion values, active power loss, actual and displacement power factor percentages, and resonance damping capabilities. These indices were handled as non-linear constraints in the design. Additionally, a comparative investigation of the different damped filters is presented to assess the planned filter performance. The results obtained confirm the success of the filter design proposed in this paper. [ABSTRACT FROM AUTHOR]

Defossilizing the energy and industrial systems and transformation towards a circular economy have versatile impacts on the availability and demand of alternative resources. However, systematic analyses on these interrelations are very sparse. Thus, this paper investigates the interlinkages between the energy transition and the transformation towards a circular economy in Germany. The study is based on material flow analysis models of the steel and cement industries as well as the coal and lignite power plants in order to depict a comprehensive description of the material, energy and emission flows of these sectors and their interdependencies. The analyses prove the inherent interrelation between energy transition and circular economy. On the one hand, energy transition does not only bear various techno-economic challenges and have substantial effects on the production costs, but it also affects the availability of secondary materials. For instance, the coal phase-out and the introduction of green steel imply that the current supplies of blast furnace slag, fly ash and FGD gypsum cannot be maintained. On the other hand, circularity approaches can also influence the energy transition. For example, banning single-use plastics and increasing recycling rates of plastics can deprive the cement industry from significant amounts of energy resources with low-carbon footprint. Contrariwise, CO 2 recycling can promote the energy transition by means of mitigating CO 2 emissions via carbonation. Therefore, the study can help policymakers to understand the indirect effects of existing and future policies. Also, it can help to develop future strategies to handle associated impacts and avoid material shortages. [ABSTRACT FROM AUTHOR]