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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

Automated manufactures, nuclear power plants, railroad transportation, and spacecrafts can be reviewed as examples of very complex modern technical systems. The subsystems of such systems contain dozens of sensors, actuators, etc. To achieve the system goals we need to implement the specific schedule of right synchronized and logically coordinated processes involving various onboard devices. The system control means should be autonomous and dependable. It must implement needed control to complete system tasks in the changing environment and even in case of some faults of the equipment. The other important aspect is compliance with the limited levels of the available onboard resources during the whole mission - electric power, etc. The paper discusses the notion of such autonomous dependable control. The aspects of complexity and requirements for "intelligence" to provide needed control having been analyzed. We discuss perspective approaches to synthesis and verification of such intelligent and dependable control. [ABSTRACT FROM AUTHOR]

This research paper aims at applying modern control techniques to study the stability and power consumption of electric vehicles. The main purpose is to propose an artificial neural network controller to estimate the direct yaw moment required for stabilizing the dynamics of electric vehicles with four in-wheel motors. Only one state variable, that can be measured using a cheap and standard sensor, is used to define the error signal for the controller. The algorithm of equality-constrained quadratic program is used to split the estimated yaw moment onto the four in-wheel motors. The neural network controller is trained using data obtained from fuzzy logic controller. In addition, the classical sliding mode control is applied for the sake of comparison. Simulation results prove that in comparison to classical controller, the mentioned modern controller provides better stability of the electric vehicle's lateral dynamics and lower usage of electric power for the motors. [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]

This paper substantiates application of pit conveyors at the stages of pit rework and underground mining beyond-edge reserves. Heavy pit lift transport is not efficient under conditions of the low enterprise output capacity. Thus, variants of further increasing its efficiency shall be stipulated. The paper considers the methods to increase efficiency of pit conveyors depending on the type of the electric drive and exploitation mining conditions. It provides calculation of electric power costs dependent on adjustability of the electric drive, presence of the pit to accommodate a stock dump at the site and replace electric motors. It is found that it is reasonable to replace electric motors of the conveyor with those of a higher energy efficiency class at the stage of the pit rework during underground mining beyond-edge reserves at the operation of the inclined conveyor. [ABSTRACT FROM AUTHOR]

Going back and taking a quick glance at the history of developed countries prove that prosperity of any society is tightly intertwined with resiliency and sustainability of its preliminary infrastructures. Surely, in modern societies, electricity is among the most important infrastructures whose resiliency and sustainability are a key driving force toward development of the society. This is verified by the fact that since the industrial revolution, per capita electricity consumption has been taken as a key index showing the level of economic development and standard of living in a country. This paper focuses on tile concept of resiliency and sustainability of electric power systems. The paper, initially, introduces the concept and evaluation procedure of power systems resiliency. Then, it strives to introduce tile most challenging issues faced by resilient and sustainable power grids. The challenging issues are electricity load growth, energy crisis, environmental emissions and climate changes, unexpected events, aging infrastructures, and cyber challenges. Then, the most effective solutions proposed by power industry scientists and engineers are discussed. The solutions are asset management, renewable energy resources, demand response, controlled islanding and micro-grids, automation, self-healing, and monitoring systems. Finally, a typical sustainable and resilient power system is described. [ABSTRACT FROM AUTHOR]

This paper has made a comprehensive introduction to the new national standard GB50116, which is the code for the design of automatic fire alarm system. It gives a specific comparison between GB50116-2013 and GB50116-98. And the content of GB50116-2013 is more comprehensive than GB50116-98. The paper makes detailed analysis to their specific contents, and gives comparative analysis to these two standards, including terminology, basic provisions, design of fire linkage control, selection of fire detectors, settings of system equipment, system power supply, and cabling, etc. [ABSTRACT FROM AUTHOR]

The paper compares transient processes of self-starting non-controlled and variable frequency electric drives (VFED) at short-term power supply interruptions. It offers simplified dependencies for calculating duration of transient processes of the individual self-starting asynchronous motor. The study defines reasons for increasing speed recovery time of this assembly. It distinguishes main differences of self-starting the variable frequency and non-controlled electric drives with or without the Flying Start function in the algorithms of the frequency converter. Results of the experimental studies of self-starting high-voltage VFED with the Power Flex 7000 frequency converter are presented. The paper proposes guidelines to provide continuous operation of the essential units at the power supply interruptions. It also emphasizes developments aimed to improve VFED self-starting conditions. [ABSTRACT FROM AUTHOR]

The paper presents an assessment of the functional mechanisms that are part of the security system for the power grid control. The security system, its components, and the real time processes for the control of electricity supply were defined. In particular, SCADA protocols used in the control system and mechanisms for transferring them between the control center and actuators were identified. The paper also includes presentation of a test environment that is used for developed security mechanisms evaluation. In the last fragment of the paper, the test scenarios were formulated and the results obtained in the cyber security system were shown, which cover security probes reaction delay, forged malicious IEC 60870-5-104 traffic detection, DarkNet and HoneyPot interception of adversary actions, and dynamic firewall rules creation. [ABSTRACT FROM AUTHOR]

Damaged insulators may affect the mechanical and electrical performance of an electric power grid, which can lead to the flow of leakage currents through the line supports. This increases electrical losses and voltage drop in the power grid. It also poses a risk to human safety. Thus, it is crucial to monitor and inspect the condition of insulators to detect degradation or damage. However, the traditional method of inspection is inadequate in meeting the growth and development of the present electric power grid. Hence an automated system such as the computer vision method is presently being explored as a means to resolve this crisis safely, speedily and accurately. This paper presents a method that distinguishes between defectuous and non-defectuous power-line insulators. Active Contour model is applied for insulator segmentation in order to determine insulator region of interest. Local binary pattern is used for feature extraction from the insulator region of interest which is then fed to the support vector machine classifier for classification. An accuracy of 94.1% was achieved when morphological operation is used in combination with active contour model for segmentation based on the groundtruths. In addition, local binary patterns feature extraction method outperformed gray level co-occurrence matrix when used with support vector machines. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things Industry in Indonesia is increasing rapidly from year to year. One of the uses in this industry is being able to carry out monitoring, control, and analysis. The smart home is one of the applications of IoT that is used in households to make it easier for humans to monitor and control all devices at home. Most homes in Indonesia have various commonly used resources,these are water, electricity, and gas. These resources will run out quickly if humans cannot use them properly. Therefore we need to monitor and control the resources we have at home. One of the resources that will be monitored and controlled in this study is electricity resources because it is more widely used in every corner of the house and almost all household devices use these electricity resources. This research proposes a way to do electricity use effectiveness with monitoring and power control using cloud-based IoT. The proposal discussed in the paper is expected to have other benefits besides saving electricity resources, but it is also expected to reduce the cost of energy use at home. [ABSTRACT FROM AUTHOR]

Abstract Thermal energy recovery systems, based on thermoelectric generator (TEG) units, used in the internal combustion engine (ICE) vehicles, enable recovery of wasted heat energy, generated from the combustion process by converting it into electrical energy. Recovery systems are placed into the exhaust subsystems, just before and after the catalytic converters, where temperatures range is from 200 °C to 400 °C. This modest arrangement is a result of TEG units working temperatures and limitations. In the early stages TEG units were placed into the exhaust mufflers where the temperatures are around 200 °C. This paper is a report on the green transportation project that carries on from the previous research, testing and validation conducted in the School of Engineering. We investigated process where, the exhaust port of the engine cylinder head was identified as an optimal location to place the latest technology, high temperature, TEG units. Maximum working temperature of these new devices is 850 °C so that it is safe to place them into the exhaust port of the cylinder head, where the temperatures are around 800 °C. [ABSTRACT FROM AUTHOR]

This paper presents energy flows management of multiple electric vehicles charging and its impact on a microgrid. The studied low voltage grid comprises also two photovoltaic generators and non-dispatchable loads. Three concepts using electric vehicles batteries as a distributed storage and bidirectional energy transfer in the Smart Grid are considered: Vehicle to Home, Vehicle to Vehicle and Vehicle to Grid. The potential for implementation of flexible microgrid energy flow management strategy for reduction of peaks in power consumption, absorption or production of reactive power (when required) and improving the power quality in the studied microgrid are estimated. For this purpose a set of usual load profiles of electric vehicle charging modes are studied and presented. Simulation results demonstrate the potential of electric vehicles as a flexible load and energy storage for peak load shaving in microgrids. [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]

Sustainable energy consumption is a research area of a large interest in last years. New energy-efficient lighting technologies exist, which can significantly reduce household electricity consumption, but their adoption has been slow. A considerable number of international studies show that sustainable development scenarios will be realistic if they involve the human behavior. The paper provides a solution to integrate consumer behavior with techno-economic energy issues, inside a complex energy model. This approach aims to reduce the error, from the demand side of the energy model. Consumer behavior in energy consumption is described by using specific attributes of energy technologies, namely virtual technologies. The GAMS/TIMES/VEDA energy modelling software tool has been used to integrate the techno-economic energy specific data and consumer behavior, in order to achieve the research purposes. A sociological survey about Romanians’ behavior in energy consumption has been conducted and its results have been integrated in the energy based model, in form of virtual technologies. This approach, known as “Social MARKAL”, allows the analyst to evaluate the possible contribution of information campaigns in changing lighting consumers’ behavior and effect of technology switch. The model is developed for the period 2010-2026, and is implemented by using GAMS/TIMES/VEDA software platform. The results presented this paper focuses on the household lighting technologies. [ABSTRACT FROM AUTHOR]

The paper reviews the most relevant standards, guidelines, frameworks and methodologies about risk assessment and management, that could be useful to enhance power companies risk assessment practices. It will be presented the criteria used in evaluation methodologies - especially on how to handle threats and vulnerabilities, standards and prescription coverage, along all risk management process - identification, quantification, prioritization, treatment and control, and areas of specific application. The need for this paper stems from the recent proliferation of standardization activities dealing with risk management issues. While this signals a growing attention to risk management and to its integration within the broader organizational context, it has led to the creation of multiple and sometimes conflicting standards and guidelines. This in turn calls for a review and rationalization of the current publications about risk management, specifically considering those directed to the power sector. Moreover, it is necessary to evaluate whether and to what extent the existing generalist approaches and technical methodologies are applicable to electrical networks planning and operation and to their specificities. The review of the existing standards and guidelines will serve as the base for the development of a more integrated and comprehensive methodology for risk assessment for electrical networks planning and operation. [ABSTRACT FROM AUTHOR]

Romanian Power Grid Company "Transelectrica" is the Transmission System Operator (TSO) and the metering operator for the wholesale electricity market. The paper provides a description of the new Pan European Network Codes and new Power Quality (PQ) Romanian regulations including wind power plants (WPP) and photovoltaic power plants (PVPP), in order to meet the requirements presented in the international PQ standards. The paper presents also the features of Power Quality Monitoring System (PQMS) of Transelectrica installed in the Common Coupling Points (CCPs) of wind power generation. Transelectrica's PQ strategy includes the process of acceptance testing of wind and solar power generation. This assures wind and solar secure connection. Some aspects of PQ Management which has to integrate the technical, legal issues/regulatory and managerial aspects of PQ in the entire National Power System for all operators in the energy market are underlined in the paper. Finally, the conclusions and recommendations and the next steps in the power quality national strategy have been drown. [ABSTRACT FROM AUTHOR]

DVR is one of the custom power devices for compensating power quality indices. A self-tuning controller with a bi-objective structure is presented for controlling the DVR compensator in order to improve the THD and voltage sag indices of a sensitive load in the network. In this paper, the emotional controller which is based on emotional learning of human brain is proposed for controlling the DVR compensator. This controller has such a structure that makes it capable of considering a second objective in the control process of the system. So far, this capability of the emotional controller has not been used in any researches. The results of the paper demonstrate that compensating and controlling the voltage THD signal in the control process has caused more improvement in the voltage sag of the sensitive load. It was reported that the performance of the emotional controller depends on the selection of the values of its coefficients. Therefore, in order to better improve the proposed controller, these coefficients are tuned by an optimization algorithm. Teaching–learning-based optimization algorithm is considered as optimization algorithm to regulate these coefficients. According to simulation results, it works significantly better than classic PI controller and some intelligent controllers that have introduced in other researches already. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for probabilistic transmission network expansion planning in Smart Grid. The proposed method makes use of Controlled Nondominated Sorting Genetic Algorithm (CNSGA-II) of multi-objective meta-heuristics (MOMH) to calculate a set of the Pareto solutions. In recent years, electric power networks increase the degree of uncertainties due to new environment of Smart Grid with renewable energy, distributed generation, Demand Response (DR), etc . Smart grid planners are interested in improving power supply reliability of transmission networks so that probabilistic expansion planning approaches are required. This paper focuses on a multi-objective problem in probabilistic transmission network expansion planning. The multi-objective optimization problem may be expressed as multi-metaheuristic formulation that evaluates a set of the Pareto solutions in Monte Carlo Simulation (MCS). In this paper, CNSGA-II is used to calculate a set of the Pareto Solutions. The proposed method is successfully applied to the IEEE 24-bus reliability test system. [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]