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Motors that work with high voltage and consume a lot of power, specifically those of 6 kV and 650 kW, generate harmonics of voltage and current harmful to the electro-energy system, causing problems in its correct operation. In this study, the quality of the electrical energy of a motor having those characteristics is analyzed by measuring voltage, current intensity, power factor, active power, reactive power, as well as voltage and current harmonics. Results show that the main problem lies in the fact that the total current harmonic distortion reached a value of 17.1%, which is above the maximum value permissible according to the standard for the motor object of study. However, the total voltage harmonic distortion resulted within the allowable values, reaching a maximum of 3.3%. To mitigate the harmful effects of the total current harmonic distortion, passive and active filters were designed and simulated to improve the motor's operation and solve the problems. For the design and simulation of the filters, MATLAB R2015a software and LabView 2014 software were used. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [ABSTRACT FROM AUTHOR]

During the construction of smart grids, due to technological limitations, traditional sequential control systems have been unable to meet the needs of grid operation, exposing problems such as single control methods and functions, low operational efficiency, inability to independently complete complex tasks, and poor anti-interference ability, which in turn affect the power supply quality and operational stability of the grid, and fail to truly achieve the reliability, safety, and reliability of smart grids Economic and efficient goals. For this purpose, the author describes an adaptive intelligent control method for an integrated biomass energy heat conversion system consisting of more than one suction biomass gasifier, biomass gas burner, and steam boiler. The system uses PLC as a tool and takes the boiler outlet steam pressure as the control parameter. Based on the changes in the boiler outlet steam pressure, it automatically adjusts the feed and gas production of the gasifier, and subsequently adjusts the air supply of the burner. Ultimately, it achieves the goal of automatically adjusting the boiler heating volume with changes in steam pressure. The integrated heating and adaptive intelligent control system based on biomass gasification has been reliably verified in industrial applications. [ABSTRACT FROM AUTHOR]

The power supply is crucial in the present day due to the negative impacts of poor power quality on the electric grid. In this research, we employed deep learning methods to investigate the power factor, which is a significant indicator of power quality. A multi-step forecast was developed for the power factor in the power supply installation of a hot rolling mill, extending beyond the horizontal line. This was conducted using data obtained from the respective electrical supply system. The forecast was developed via hybrid RNN (recurrent neural networks) incorporating LSTM (long short-term memory) and GRU (gated recurrent unit) layers. This research utilized hybrid recurrent neural network designs with deep learning methods to build several power factor models. These layers have advantages for time series forecasting. After conducting time series forecasting, qualitative indicators of the prediction were identified, including the sMAPE (Symmetric Mean Absolute Percentage Error) and regression coefficient. In this paper, the authors examined the quality of applied models and forecasts utilizing these indicators, both in the short term and long term. [ABSTRACT FROM AUTHOR]

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols. [ABSTRACT FROM AUTHOR]

With the high proportion of new energy access and the increasing demand for load electricity, efficient and reasonable control of battery energy storage systems (BESS) in the power grid is the key to promoting new energy consumption, improving the quality and economy of power supply in the power grid. To solve the optimization control problem of BESS under multiple power service objectives, a two-stage optimization control method for BESS based on multi-service attribute utility evaluation has been proposed in this paper. Firstly, based on various power service demands and grid operation constraints, a multi-objective optimized scheduling model for BESS has been established; Then, to solve the complex coupling problem between multiple optimization objectives of BESS, a two-stage optimization structure has been designed that the changes of various service indicators under different optimization objectives have been recognized in the first stage, and the entropy weight method was used to weight each optimization objective in the second stage objectively; Finally, in the IEEE 33-bus simulation example, compared with the suboptimal result under each singleobjective method, the relative error of the power line losses, the maximum voltage deviation, the curtailment of new energy and the economics of power grid of this algorithm has been reduced at least 2.31, 1.11, 2.90, and 1.05 times, respectively. Meanwhile, the performances of active power reserve and reactive power reserve are better than most single-objective optimizations. [ABSTRACT FROM AUTHOR]

Real-time monitoring of electricity usage details through load monitoring techniques is a crucial aspect of smart power grid management and monitoring, allowing for the acquisition of information on the electricity usage of individual appliances for power users. Accurate detection of electricity load is essential for refined load management and monitoring of power supply quality, facilitating the improvement of power management at the user side and enhancing power operation efficiency. Non-intrusive load monitoring (NILM) techniques require only the analysis of total load data to achieve load monitoring of electricity usage details, and offer advantages such as low cost, easy implementation, high reliability, and user acceptance. However, with the increasing number of distributed new load devices on the user side and the diversification of device development, simple load recognition algorithms are insufficient to meet the identification needs of multiple devices and achieve high recognition accuracy. To address this issue, a non-intrusive load recognition (NILR) model that combines an adaptive particle swarm optimization algorithm (PSO) and convolutional neural network (CNN) has been proposed. In this model, pixelated images of different electrical V-I trajectories are used as inputs for the CNN, and the optimal network layer and convolutional kernel size are determined by the adaptive PSO optimization algorithm during the CNN training process. The proposed model has been validated on the public dataset PLAID, and experimental results demonstrate that it has achieved a overall recognition accuracy of 97.26% and F-1 score of 96.92%, significantly better than other comparison models. The proposed model effectively reduces the confusion between various devices, exhibiting good recognition and generalization capabilities. [ABSTRACT FROM AUTHOR]

The study investigates the implementation of novel Neuro-Fuzzy controllers to maintain the power quality for standalone Photovoltaic (PV)-Electrolyzer-Fuel Cell- Battery based power generation systems. Standalone solar based power generation systems are widely becoming popular particularly in the remote areas with no connectivity to a grid. There are challenges to maintain the power output to meet the demand in these standalone systems because of the random nature of solar irradiances, variation of load and no irradiance during night time. The battery bank is required to store the excess energy for use later when required. The battery bank is integrated into a standalone system through a bidirectional DC to DC converter. However, large batteries are costly and require maintenance. Hence, electrolyzer and fuel cell are also integrated with respective DC to DC converters to make a cost effective operation. Small size battery bank is used to stabilize dc-link voltage during transients due to slow dynamics of electrolyzer and fuel cells. The maximum power point tracking (MPPT) device and perturbed and observed algorithm is used for the PV system to operate at maximum utilization. In this paper Neuro-Fuzzy controller based novel controllers are implemented to inverter and DC to DC converters for supplying quality power to both three phase and single phase loads at AC load bus. The presented results are examined through hardware-in-loop on the platform of OPAL-RT to investigate the proposed controllers in all the possible scenarios of a 1 MW standalone system. Results show that the proposed controllers improve the power quality and eliminate the frequency oscillations from the system voltage. [ABSTRACT FROM AUTHOR]

The evaluation of the quality of electric power distribution services in Brazil is regulated and monitored by the National Electric Energy Agency ANEEL, which uses metrics related to the duration frequency of power interruptions that occur in the power utilities' networks. The methodology applied by the agency to establish financial compensation due to violations of the quality standards does not take into consideration the consumers' production industry when establishing compensation for the transgression of service quality indicators. This study will analyze a case study of a group of industrial consumers linked to agribusiness in the southern region of Brazil, which have strongly seasonal use of distribution networks and are scattered in large, dispersed areas. Based on the evaluation of the impact of service quality indicators on financial compensation, a regulatory mechanism is proposed in the form of an interruptible tariff duly quantified in the form of a non-linear programming problem to find a discount range for uninterruptible tariffs. The results obtained with the real data for the group of irrigating consumers demonstrate the feasibility regarding the application of the proposed approach, whether due to the discounts offered or even the repercussions of the calculated financial amounts. [ABSTRACT FROM AUTHOR]

The railway power conditioner (RPC) is a promising technology to improve the regenerative braking energy (RBE) utilization and power quality of the traction power supply system (TPSS). The hybrid energy storage systems (HESS) play a key role in the economic operation of TPSS due to the high cost of the system. The capacity and power of HESS are the critical issues affecting the system performance and cost. Therefore, a hierarchical energy management model is proposed to optimize the HESS sizing and the scheduling of TPSS with RPC. At the master level, a nonlinear mathematical model is built to minimize the HESS cost, which includes capital cost, replacement cost, and recovery cost. At the slave level, considering voltage unbalance as a constraint, a mixed‐integer linear programming model is established to minimize the electricity cost of railway operation. In order to solve the hierarchical nonlinear programming problem, an improved particle Swarm optimization (PSO) algorithm with a GUROBI solver embedded is proposed. Finally, the results show that the 14.08% cost reduction is achieved, and the standard limit value of voltage unbalance is satisfied. [ABSTRACT FROM AUTHOR]

The centralized power grid abides a heavy burden while consumers expect an uninterrupted reliable power supply. Further complexities increase by a satisfying reduction in carbon emissions, increased efficiency within the national grid and power supplied to remote communities. This has led to alternatives being sought for centralized power generation, which is prone to outages due to long distance transmission. This is a substantial contributor to global carbon emissions, has large transmission losses and is often not a practical solution when supplying remote communities. Distributed generation (DG) becomes an alternative in this context by producing power close to its point of consumption, often utilizing carbon neutral, renewable energy (RE) sources (sun, wind). Apart from many benefits of the microgrid, there are several problems and challenges in the integration with power systems in terms of control and stability issues. To overcome such issues, Energy Storage Systems (ESSs) may be the next solution level in the enhancement of system stability with quality power supply. In this paper, a brief review of the microgrid architecture, their challenges, the control topologies, and techniques have been discussed. Hybrid energy storage systems (HESSs) characterized by the coupling of energy storage technologies are emerged as a solution to achieve the desired performance by combining the appropriate features of different technologies. Exploration of different researchers confirms that the control of ESSs has an effective role in stability analysis, economic analysis of MGs. A discussion about the control methods of Microgrid with ESSs and future trends are also presented. As single ESS technology cannot fulfill the desired operation due to its limited capability and potency in terms of lifespan, cost, energy and power density, and dynamic response, different ESSs operation configurations considering storage type, interface, control method, and the provided service have been proposed. [ABSTRACT FROM AUTHOR]

This paper proposed a simplified modeling approach for a power quality (PQ) assessment of Electric Submersible Pumps (ESP) systems supplied by the two-level, the neutral-point-clamped three-level, and the cascaded H-bridge (CHB) multilevel inverter VFD topologies. The VFD switching function models and their analytical expressions are proposed to understand how they can create high-frequency components that might excite the resonance mode in a transmission cable or a rotating shaft system. Voltage, current, and motor airgap torque harmonics induced by each VFD topology in a balanced operation mode are derived and correlated to the PWM carrier and motor operating frequencies. The motor airgap harmonics are calculated based on Concordia's transformation of voltages and currents in αβ-plan. These harmonic components are represented in the form of Campbell diagrams. An analysis of harmonics under unbalanced conditions was also conducted in a CHB VFD topology-powered ESP system with failed and bypassed cells. The investigated modulation technique is a neutral-shift PWM method that enables the system to operate balanced line-line voltages even if the line-neutral voltages are unbalanced. The effects of modifying the electrical spectrum using the neutral-shift PWM method on electrical and mechanical spectra are analyzed. The results of the Matlab/Simulink-based simulation show that the proposed full ESP system model is highly accurate in both normal and failure modes. The results are consistent with theoretical predictions and are graphically shown in the time and frequency domains for easy analysis. Hybrid experimental–numerical results on a reduced-scale laboratory setup are also discussed to confirm the correctness of the suggested developments. [ABSTRACT FROM AUTHOR]

The test quality of noncontact instruments is an important issue that has aroused social concern at present, which is also widely used in the power quality management of telecommunication systems. The fuzzy logic algorithm can be used in power quality testing to analyze the power quality of electrical equipment. Based on the advantage of the fuzzy logic algorithm, this paper studied the quality control strategy and evaluation algorithm of noncontact instrument testing and drew a conclusion. In terms of the investigation on the safety of power quality measurement, it was concluded that the method of noncontact instrument testing power quality improved the safety of voltage quality, current quality, power supply quality, and power consumption quality. In the investigation of the accuracy of the power quality measurement, it was concluded that the noncontact instrument was used to measure power quality, which greatly improved the accuracy of measurement. In terms of the investigation on the rapidity of the power quality measurement, it was concluded that the use of the noncontact instrument to test power quality could greatly improve the rapidity of current quality measurement and that the rapidity of the power quality measurement could be improved by more than 70%. In the aspect of economic investigation of the power quality measurement, it was concluded that the use of the noncontact instrument to test power quality could greatly improve the economic efficiency of the power quality measurement. In the aspect of fuzzy logic evaluation in power quality management, it was concluded that most of the power quality grades of the selected research objects were excellent. Therefore, the fuzzy logic method was very meaningful for power quality evaluation. [ABSTRACT FROM AUTHOR]

Power generation using renewable energy sources is becoming popular in current scenario due to their capability of reduction in carbon emission as well as reliable power supply to consumers. However, power generation from a single renewable energy source cannot able to provide stable power. Hence, integration of renewable energy sources-based power generations needs to be established for providing continuous power supply to consumers. Solar energy and wind power are two major available sources on earth, hence, photovoltaic (PV) and permanent magnet synchronous generator (PMSG) based wind power generation system is considered in this paper. On the other hand, many loads are AC loads that required conversion from DC to AC such as inverters. Generally, multilevel inverters can able to provide quality supply to loads than normal inverters. However, the number of switches used in the multilevel inverter is more and also increasing size and complexity by increasing the level of the inverter. Therefore, an aligned multilevel inverter is implemented for a PV-Wind-based stand-alone system that has a smaller number of switches. Both solar irradiance and wind speed are depending on weather conditions and are unstable, hence a battery bank is incorporated with the help of a bidirectional DC to DC converter to provide an uninterrupted power supply to loads. A novel controller for 7 levels aligned inverter is implemented in this paper to supply quality power to loads under various scenarios. The proposed Aligned Multilevel Inverter needs half of the switches when compared to existing multilevel inverters as diode-clamped, flying-capacitor, and cascaded H-Bridge topologies. The hardware-in-loop is developed with OPAL-RT to present results under various case studies. [ABSTRACT FROM AUTHOR]

The Proton Exchange Membrane Fuel Cells (PEMFCs) are one of the most effective and optimistic renewable energy source and they are extensively used in automotive applications. In the past, many researchers focused on solving the issues of extracting maximum power from fuel cell, controlling the speed and reducing the torque ripple of Brushless DC (BLDC) motor for fuel cell based Electric Vehicle (EV) systems. However, it is challenging to fine-tuning the gain parameters in the existing works MPPT approach and extracting maximum amount of energy. Additionally, it has limitations like unregulated voltage, problems of large overshoot, slow tracking speed, output power fluctuation, computational complexity and intricate modeling. Thus, the proposed work aims to create a revolutionary methodology called Unified Firefly Ersatz Neural Network (UFENN) - Maximum Power Point Tracking (MPPT). The UFENN is a kind of optimization-based machine learning technique that was created for efficiently optimizing the parameters to extract the maximum energy from the fuel cells. Furthermore, in order to control the output voltage with the least amount of power loss, an Interleaved SEPIC converter is also used in this work. During performance analysis, an extensive simulation results have been taken for validating the results of the proposed scheme by using various evaluation indicators. • Maximum power extraction from PEMFC. • An improved optimization based AI mechanism is developed for MPPT controlling. • The output voltage is highly boosted and regulated with the use of interleaved SEPIC converter. • An extensive simulation is carried out to assess the performance of the proposed model for electric vehicle application. • Enhancement of quality in electric power at EV systems. [ABSTRACT FROM AUTHOR]

Conventionally, DC arc furnaces are fed by thyristor rectifiers to control the level of current or power transferred to the load. With the advancement of high-power transistors, other structures such as diode rectifiers and DC choppers have been introduced and developed for the feeding system of DC electric arc furnaces. In this paper, the effect of different power supply systems on power quality indexes is discussed. In this regard, two types of feeding systems are considered, including a power supply system based on thyristor rectifiers and a power supply system based on diode rectifiers and DC choppers. In addition, different control methods, including constant current control (CCC) and constant power control (CPC), on DC electric arc furnaces are applied. For evaluating the power quality indexes, voltage and current harmonic distortion, unbalance of voltage and current, and power factor on the AC side are investigated for different power supply systems. Simulation results were performed with PSCAD/EMTDC software. It is shown that the power supply systems with diode rectifiers and DC choppers have superiority in comparison to the types based on thyristor rectifiers. According to the results, the average current THD is reduced from 16.55% in the thyristor rectifier to 8.40% in the chopper rectifiers with the CCC method and from 19.27% in the thyristor rectifier to 7.38% in the chopper rectifiers with the CPC method. Moreover, the average voltage THD is reduced from 5.67% in the thyristor rectifier to 3.02% in the chopper rectifiers with the CCC method. The result is similar to the CPC method. Furthermore, for a specific power supply system, the harmonic distortion is lower in the case of the CPC method than in the CCC method. [ABSTRACT FROM AUTHOR]

The characteristics of traction network (TN) of the three‐phase 3‐kV traction power supply system (TPSS) are: three‐phase conductors are flat layout (layout‐1, layout‐2); the material and cross‐section of conductors are ferromagnetic and irregular, respectively. So the phase impedance matrix (PIM) is unbalanced and the closed‐form formula for conductor impedance is hard to get. To address these issues, firstly, the PIM is derived, and the cause of unbalance is revealed. From the perspective of structure optimization, a new flat layout (layout‐3) and an equilateral triangle layout (layout‐4) is proposed. Then, to calculate the wideband frequency self‐ and mutual impedances of the special phase conductors, a frequency‐ and current‐dependent closed‐form formula based on 4‐parameters equivalent tubular conductor (ETC) model is proposed. Furthermore, an equivalent TN circuit model at 50 Hz is established to calculate the receiving‐end voltage and its negative sequence voltage unbalance factor (NSVUF). Finally, using these two indexes, the performances of the layout‐1 to 4 are evaluated and compared. The results show that the conductor spacing and power supply frequency affect the two indexes much while the dimension of the power supply rail (PSR) not; layout‐4 can completely balance the receiving‐end voltage and improve its magnitude which can enhance the power supply capability. [ABSTRACT FROM AUTHOR]

This study proposes the integration of a supercapacitor (SC) with the DC link of a three-phase four-wire active power filter (APF) by using an interfaced three-level bidirectional buck-boost converter controlled by the fuzzy control approach. APF is a flexible alternating current transmission system (FACTS) device that enhances power quality in the electrical network by reducing the load current harmonics and compensating the reactive power. Regulating the DC voltage of the APF's DC link and absorbing fluctuations in compensated reactive power during disturbances are the major objectives of the integration of an SC circuit to APF. The studied model is a wind farm of Gabal El-Zeit with a total capacity of 580 MW, connected to unbalanced nonlinear loads. The Gabal El-Zeit wind farm is divided into three projects with a capacity of 240 MW, 220 MW, and 120 MW. The model is simulated by the MATLAB/SIMULINK program, and the effectiveness of the proposed methodology is proved by applying different types of faults such as single-line-to-ground, double-line-to-ground, and three-line-to-ground faults. In addition, an additional SC circuit with a two-level converter is connected to the generators coupled to the wind turbines to enhance the performance of the wind farm during disturbances. The results show that SC-integrated APF can reduce the harmonic distortion and compensate the reactive power for high or low inductive loads. Also, it can regulate the DC voltage and absorb the fluctuations in the reactive power during faults. Finally, the performance and the stability of the overall electric system are improved. [ABSTRACT FROM AUTHOR]

Automatic generation control (AGC) is essential for raising living standards because it enhances power supply quality. However, due to the constraints and challenges experienced in practice, an effective and computationally economical control method is necessary to improve AGC performance, particularly in the presence of renewable energy. Therefore, this study introduces a novel cascade controller (CC) of a proportional–integral–derivative (PIDn) controller followed by a proportional–integral (PI) controller, forming a PIDn-PI CC. This controller is used in a two-area model comprising a reheat thermal generator and a photovoltaic unit. The gains of the PI, PIDn, and PIDn-PI controllers are adjusted using the recently introduced chaos game optimization (CGO), which minimizes the objective function integral time multiplied absolute error. The CGO relies on chaos theory principals, wherein the organization of fractal geometry is perceived through the chaotic game and the fractals' self-similarity properties are considered. At first, the CGO based PIDn controller is employed, to check the suitability of CGO in dealing with AGC problems. Furthermore, several scenarios are used to confirm the effectiveness of the CGO:PIDn-PI scheme when subjected to a high load disturbance and uncertainty, which can change system parameters by ± 50%. A random load pattern is used to ascertain the proposed method's efficacy. Finally, nonlinearities, such as generation rate constraint and time delay, which have a significant impact on AGC performance, are considered. Compared with relevant current research, the suggested approach outperforms them in terms of settling time, frequency, and tie-line power deviations. [ABSTRACT FROM AUTHOR]

An efficient way to raise the proton exchange membrane fuel cell's (PEMFC's) power generation efficiency and power supply quality is to use maximum power point tracking (MPPT). Conventional MPPT approaches often have difficulty producing an effective control effect due to the PEMFC's inherent nonlinear characteristics. Another challenge for systems that track maximum power points is dealing with fast changes in operational conditions that affect FC's maximum power point (MPP). The main contribution of this study is the introduction of two artificial intelligence-based MPP control approaches for fuel cells operating under rapidly changing operating conditions. These methods are based on imperialist competitive algorithm-trained neural networks and adaptive neuro-fuzzy inference systems (ANFIS) (ICA NN). The proposed approaches determine the fuel cell voltage that corresponds to the maximum power point. Following that, a fuzzy logic controller is used to modify the duty cycle of a DC/DC boost converter for FC MPP tracking. The MATLAB environment is used to run simulations. The results of the proposed method are compared with those of the conventional fuzzy method. The results demonstrate that the suggested solutions function excellently in both normal operating conditions and quickly varying operating conditions. On the other hand, the suggested approaches can quickly locate and monitor the MPP of FC. Additionally, the suggested techniques increase the FC system's efficiency by absorbing more power. [ABSTRACT FROM AUTHOR]

The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system's supervisory control and data acquisition's (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model's forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power. [ABSTRACT FROM AUTHOR]

The development of AC distribution systems provides for the seamless integration of low-voltage microgrids with distributed energy resources (DERs). This poses new challenges for the control of normal, emergency, and post-emergency states of microgrids, calling for the creation and development of information and communications technology infrastructure. Power converters/inverters that are used to integrate renewable DERs lack inertia. Along with them, fossil fuel-fired generation units are also being integrated into microgrids. These include gas generator sets, diesel generator sets, and microturbines, having small (up to 1–2 s) values of mechanical inertia constants—Tj. This leads to an increase in the rate of transients by a factor of 5–10. Under these conditions, the technical requirements for the speed of automatic power flow control systems, as well as the methods they rely on, have to be reconsidered. Microgrids include DC microgrids, AC microgrids, and hybrid (AC-DC) microgrids. In the case of hybrid microgrids, DERs are connected to the DC grid and are integrated into the AC grid through a common inverter. The complexity of the task of microgrid control is due to the need to choose properly the type and extent of control actions so as to prevent the emergence and development of accidents. The employed control methods must ensure the reliable power supply to consumers and the quality of power in microgrids, as well as the reliable operation of the external distribution systems into which they are integrated. The article gives an overview of control methods for low-voltage AC and AC-DC microgrids, which allow one to tackle effectively solve the tasks. [ABSTRACT FROM AUTHOR]

Although a switched reluctance generator (SRG) is not the mainstream wind generator, it possesses the application potential and is worth developing for its many structural merits and high developed power ability. This paper presents a wind SRG-based bipolar DC microgrid having grid-connected and plug-in energy supporting functions. First, a surface-mounted permanent magnet synchronous motor (SPMSM)-driven wind turbine emulator (WTE) is established. Next, the wind SRG with an asymmetric bridge converter is developed. Good generating characteristics are obtained through proper designs of power circuit, commutation mechanism, external excitation source, voltage and current controllers. Third, a DC/DC boost interface converter and a bipolar voltage balancer are constructed to establish the 500 V microgrid bipolar DC-bus. To preserve the microgrid power supplying quality, a battery energy storage system (BESS) with bidirectional DC/DC interface converter is equipped. A dump load leg is added across the bus to limit the DC-bus voltage under energy surplus condition. In load side, a three-phase bidirectional load inverter is developed, which can be operated as a single-phase three-wire (1P3W) inverter or a three-phase three-wire (3P3W) inverter. Good sinusoidal voltage waveform and regulation characteristics are obtained using the proportional-resonant (PR) control. The microgrid to load and microgrid to grid operations are conductible. Finally, to further improve the powering reliability of microgrid, a three-phase T-type Vienna switch-mode rectifier (SMR) based plug-in energy supporting scheme is developed. When the microgrid energy shortage occurs, the possible harvested energy can be used to supply the microgrid. [ABSTRACT FROM AUTHOR]

The relevance of the work is connected to the energy efficiency of specialized vessels of the technical fleet. The purpose of the study was to determine and evaluate the power quality indicators associated with the non-sinusoidal shape of the voltage and current curves in the electrical power system of the marine platform support vessel, which contains powerful semiconductor propulsion electric drives, taking into account the inherent and parasitic parameters of the power three-phase cable lines. A simplified one-line diagram of an electric power system with a DC main bus was the object of the study, which was compiled as a result of the analysis of analog systems typical for the indicated type of vessels. The phenomenon of voltage and current distortion caused by the presence of higher harmonics generated by power semiconductor converters in a three-phase ship network was the subject of the research. For the experimental study of the quality of electric power according to the simplified one-line scheme of the electric power system in MATLAB Simulink, its model was created. Based on the proven methods of calculating the ship's electrical equipment, a methodology was developed for the reliable determination of model parameters. According to the results of the experiment in MATLAB Simulink, qualitative and quantitative indicators were obtained regarding the non-sinusoidality of the linear voltage and current of the three-phase network (curve shapes, amplitude spectra, distortion coefficients), and their comparative analysis with the current norms and standards was performed. In contrast to the previous ones, the methodology for assessing the quality of electricity in the studied electric power system takes into account its circuit, mode features, and the presence of a parasitic capacitance "phase to ground" of a three-phase network, and it can be used in solving similar non-trivial tasks for various similar structurally modified systems. [ABSTRACT FROM AUTHOR]

Owing to the wide distribution of rural areas, the construction of power facilities is not perfect, which poses a great challenge to the power supply stability of the rural power grid. As an important supplement, the microgrid (MG) plays a complementary role in improving the power supply quality. However, the addition of the MG increases the complexity, and how to balance the interests of the participants has become a substaintial problem. Based on the analysis of the power consumption characteristics of rural residential, agricultural and industrial areas, a game relationship model between MG operators and power users is established, and the overall optimal strategy is obtained in this paper. For consumers, it can effectively achieve the effect of 'peak shaving and valley filling' and reduce the cost of electricity. For MG operators, it can improve the consumption rate of renewable energy and increase revenue. [ABSTRACT FROM AUTHOR]

The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper presented a novel coronavirus herd immunity optimizer algorithm for the optimal accommodation of SPVDG with BESS in the PDS. The proposed algorithm is centered on the herd immunity approach to combat the COVID-19 virus. The problem formulation is focused on the optimal accommodation of SPVDG and BESS to reduce the power loss and enhance the voltage profile of the PDS. Moreover, voltage limits, maximum current limits, and BESS charge-discharge constraints are validated during the optimization. Moreover, the hourly variation of SPVDG generation and load profile with seasonal impact is examined in this study. IEEE 33 and 69 bus PDSs are tested for the development of the presented work. The suggested algorithm showed its effectiveness and accuracy compared to different optimization techniques. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as photovoltaic (PV) and wind power are widely used; however, their intermittent nature impairs power supply quality by creating frequency distortions and irregularities in voltage. Battery energy storage systems (BESS) are utilized to flatten out and relieve fluctuation issues. To prevent the need for larger storage systems and to prolong their operational life through controlled charging and discharging, a method of control for BESS charging level regulation is necessary. This study presents a solar-wind power and battery state of charge (SoC) control technique using a hydrogen electrolyzer (HE) fuel cell unit. An Intelligent Model Predictive Controller (IMPC) has been developed that utilizes a neural network (NN) plant model for predictive minimization instead of using a mathematical plant model for dynamic management of the HE output, which allows for flattened PV-wind power supply with regulated BESS charging and discharging. The IMPC accepts as inputs the intermittent renewable power and different battery attributes and intelligently manages the HE production while staying within the imposed restrictions. The NN, as opposed to a mathematical plant model, captures the dynamics of the plant with exceptionally high accuracy. Moreover, the NN plant model performance increases as the data gathered from the actual system increases. The neural network model resolves concerns with the MPC model's mathematical intricacy that occurs as the plant becomes more complicated. According to simulation results, the IMPC greatly reduces PV-wind power fluctuations, for solar power, the IMPC reduces the peak battery SoC by 26.7% and compared to FLC, the peak SoC is reduced by 11.2%. Similarly, for wind power, the peak SoC is reduced by 7.3% and is decreased 3.2% more than the FLC. To demonstrate the power smoothing effectiveness of the IMPC, the peak solar power ramp rate is reduced by 30.3% and the peak wind power ramp rate is reduced by 91.3%. The presented method encourages green hydrogen usage in the electrical sector for supplying firmed solar and wind power. [ABSTRACT FROM AUTHOR]

The nonlinear characteristics of power electronic-based loads in a power system contribute a major role in the harmonics' injection and other power quality disturbances. This affects the quality of the power supplied to consumers by distribution systems. Flexible AC transmission system (FACTS) devices at the transmission level and custom power devices at the distribution level are used for an effective power transfer. In addition to these devices, filters play a prominent role in distribution systems. This paper aims to analyze the supply voltage in the time–frequency domain to perceive the variations in signal strength, explaining the role and significance of filters in mitigating or reducing harmonics. This paper presents the significance of harmonic filters in terms of signal-processing terminology. This depicts the prominent role of filters in improving the power quality by harmonic reduction or elimination, depending upon the requirement. The mathematical transform used in this approach is the short-time Fourier transform, which results in transforming the signal into a domain giving information about time and frequency. A MATLAB Simulink environment and 'spectrogram' were used to simulate harmonic signals, and we analyzed them using a short-time Fourier transform. Different windows were used with varying window size lengths. [ABSTRACT FROM AUTHOR]

This article describes the problems associated with distributed electrical power generation and the most frequent interruptions occurring in power grids. The most common methods of improving the quality of the power supply were analyzed and the possibilities offered by energy storages in this respect were considered. The operating parameters of an exemplary PV system connected to the power grid were analyzed. For this purpose, the model implemented in the Matlab/Simulink environment was used. Based on the conducted analysis and a review of the literature, conclusions were drawn and solutions were presented which could improve the quality and the reliability of power supply. The simulations conducted focused on the co-operation of individual photovoltaic, micro-installations, with rated powers of 12.2, 19.825, and 39.65 Kw in the power grid, which also corresponds to the co-operation of several, smaller micro-installations with low density. [ABSTRACT FROM AUTHOR]

The bus voltage of the ship DC microgrid is sensitive to the change of loads, which has an influence on the power supply quality. This paper introduces a hybrid energy storage system (HESS) that is composed of a battery set and a supercapacitor set, and further studied the control method of HESS. First of all, the topological structures of the ship DC microgrid and HESS are described. Second, combined with the frequency division droop control and voltage PI control, a sliding mode control (SMC) method is proposed to control the charge and discharge of HESS based on an improved reaching law. Finally, the simulation model of the ship DC microgrid is established for the verification of the control method. Simulation results show that: (1) HESS can overcome the shortage of the dynamic response ability of the diesel rectifier generator to the steep change of load power. The supercapacitor set and the battery set successfully respond to the high-frequency and low-frequency components of the differential power in the system, respectively. (2) Compared with the traditional PI control method, SMC can reduce the current chattering of HESS and the voltage fluctuation amplitude of the DC bus. The proposed SMC method can provide a reference for the stable and reliable operation of the ship DC microgrid. [ABSTRACT FROM AUTHOR]

Multiphase electric machines are increasingly used in various industries and for electromobility. Complex systems have been developed for the control and powering of multiphase machines, which require verification. The quality of control and the power supply of electric machines is usually evaluated by analyzing various electrical parameters. On the other hand, taking into account the fact that a motor is an electrical-mechanical object, its full diagnostics should also include the analysis of vibration signals to verify the operation of the motor as a mechanical device. In this paper, a sensorless control algorithm was studied and applied to a 5-phase induction motor. Various scenarios were considered; in particular, the operation of the studied motor in the absence of one or two phases and in the case of the introduction of the third harmonic to increase the torque was analyzed. In the scenarios considered, the motor was connected to another machine and operated with no load as well as with a preset load. The results obtained were analyzed in the time and frequency domain and were related to the standards used. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and photovoltaic (PV) systems have been progressively incorporated into the grid in recent years principally due to two factors: reduced energy costs and lower pollutants. Numerous studies have investigated how integrating PV and EVs into the grid may affect specific people. It is crucial to understand that the electricity grid will experience the combined effects of PV–EV integration as PV and EV penetration increases. The primary motivation for PV's integration with Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) services is the aim to reduce charging costs from discharging; moreover, another prerequisite must be satisfied before PV arrays will be able to provide V2G services. The range between the driving limit and EV battery degradation should be reasonable. The way EVs charge and discharge will be impacted by these factors. Numerous analyses are required in order to control the power between various source and load scenarios. In order to balance grids and manage frequency, controllers such as Improved Particle Swarm Optimization (IPSO), Improved Ant Colony Optimization (IACO), and Improved Mayfly Optimization (IMO) are used. As a result, V2G/G2V helps feed electricity back into the grid. By providing the proper duty cycle ratio, the proposed controller regulates converter switching. This study allowed for the performance analysis and operation simulation of a grid-connected PV/EV/Grid system. The purpose of this system was to maximize PV self-consumption while maintaining power quality characteristics like harmonics, grid voltage/current, and power factor. [ABSTRACT FROM AUTHOR]

To adapt to frequent charge and discharge and improve the accuracy in the DC microgrid with independent photovoltaics and distributed energy storage systems, an energy-coordinated control strategy based on increased droop control is proposed in this paper. The overall power supply quality of the DC microgrid is improved by optimizing the output priority of the multi-energy storage system. When photovoltaic and energy storage work simultaneously, the proposed method can dynamically adjust their working state and the energy storage unit's droop coefficient to meet the system's requirements. In DC microgrids with energy storage units of different capacities, the proposed strategy can be used to maintain the stability of bus voltage, improve the equalization speed and accuracy of the energy storage state of charge, and avoid the shutdown of energy storage units due to overcharge or discharge. Verification of the proposed strategy is implemented with MATLAB/Simulink. The simulation results show the proposed control strategy's effectiveness in balancing energy supply and demand and reducing the time of charging and discharging energy storage units. [ABSTRACT FROM AUTHOR]

The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique's results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time. [ABSTRACT FROM AUTHOR]

The paper presents the results of the monitoring process of the charging of an electric vehicle battery pack. Battery pack charging with a capacity of 58 kWh was monitored in a single-phase 230 V /50 Hz circuit. The slow charging system used was configured to obtain a current of 10 A . During monitoring, the focus was on the recognition of the charging, considering the impact of this process on power quality and, consequently, on the reliability of electrical machines. Research results show that the monitored charges are one-, two-, or three-stage processes. The variations in the currents, power, and higher harmonic contents were observed. The effects of such variations depend on the properties of the power grid at the point of connection of the charging system. Knowledge of the variation of the voltages, currents, and active and reactive power allows for the determination of the requirements of the measuring equipment used for charging the monitoring, including the selection of discrimination/averaging time of monitored quantities. The research results also indicate the need for continuous monitoring of the power quality in the power supply circuit of electrical loads, e.g., electrical machines. Continuous monitoring supports the diagnostics of electrical machines and allows the appropriate measures to increase their reliability. [ABSTRACT FROM AUTHOR]

In the distribution network planning work, taking into account the constraints of funds and construction annual plan, there is always the prioritization problem of multiple MVDC distribution networks. Aiming at the problem, this paper proposes a method for the optimization analysis of MVDC distribution network planning objects. Firstly, a comprehensive evaluation index system of urban MVDC distribution network planning and optimization is established from three aspects of improving power supply reliability and the quality of voltage, MVDC distribution network scenario types and distribution network social benefits. Then, the qualitative indexes are quantified based on the cloud model, and the comprehensive evaluation values of each distribution network are calculated and sorted by the hierarchical additive weighting of Delphi entropy weight method. Taking the maximum comprehensive evaluation value of the scheme as objective function and the annual total investment as constraint, the construction priority of multiple MVDC distribution networks is determined. Finally, taking several distribution networks in Guangzhou, Guangdong Province, China as examples, the simulation results show that the optimal objects of distribution network based on the urban scenario demands are X4, X8 and X1, with the lowest total investment cost of 19,180 yuan, thus verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Modern electric grid usages extensive non-linear loads leading to the increased harmonic current injection in the system. This paper proposes a data-driven algorithm based on a sparse decomposition approach with the Overcomplete Hybrid Dictionary (OHD) and provides in-depth mathematical analysis to detect and mitigate harmonics in emerging electric power systems. The sparse decomposition method is widely used in image processing applications with significant advantages to big-data applications. However, its application is not properly addressed in electric grids’ applications despite massive data generation in a smart grid environment. Hence, in this paper, the performance evaluation of a sparse decomposition-based algorithm using a greedy approach is proposed and carried out in a real-time simulation environment for Distribution Static Compensator (DSTATCOM) application for the real-time detection, classification, and mitigation of PQ events to show its suitability and effectiveness. Finally, the experimental results on a small-scale laboratory setup are presented to validate the effectiveness of the proposed sparse-based control algorithm for DSTATCOM in real-time applications. The comparative result shows that the proposed sparse-based method is advantageous for off-line PQ signal processing and capable of real-time detection, classification, and control of the power apparatus for harmonics mitigation. [ABSTRACT FROM AUTHOR]