In the context of power electronic interfaces in photovoltaic (PV), fuel cell, battery, and microgrid applications, the low output voltage of the DC source necessitates a voltage-boosting inverter. This paper proposes a single-source seven-level switched-capacitor boost inverter, particularly for low-voltage applications. The proposed inverter has the capability to produce seven different output voltage levels, i.e., intermediate boosted levels, with a total gain of three times the input voltage. The inverter has the advantage of a reduced number of power switches, diodes, and a switched-capacitor unit, which allows for single-stage operation without the need for a second DC-DC converter. The operating principle of the proposed inverter is explained in detail with a complete switching state analysis, conduction path analysis, and output voltage generation. The capacitor size is calculated using a charge balance-based equation. The self-balancing capability is validated for mismatched initial voltages with a bounded steady-state ripple. To evaluate the performance of the proposed inverter in a more realistic scenario, the effects of non-ideal device characteristics are considered, and the efficiency of the inverter is estimated using a loss model. A predictive current control technique is applied to control the output current under inductive load conditions. The simulation results obtained in MATLAB/Simulink software validate the proper seven-level operation of the inverter, the self-balancing capability of the capacitors, improved output waveform quality, and current control. The proposed inverter can be extended to grid-connected applications, where conventional output filters can be applied to meet the harmonic standards.

Paralleled DC hybrid Nanogrid (DCHNG) with a communication network is configured for the profit of consumers. In this structure, parallel power converters with distinct generation units are established to supply constant power loads (CPLs). In particular, the impact of false data injection (FDI) attack on injecting false data into measurement signals is investigated from a systematical point of view. The case study is constructed from two energy storage units which supply the connected loads to DCHNG. An adaptive resilient scheme has been developed in the current paper to address the destructive effects of FDI attacks on DCHNG systems. In the first step, a detection mechanism based on the non-integer extended state observed is developed to estimate the system output while the occurrence of cyber threats is identified by the residual scheme. Then, the mitigation mechanism is applied to eliminate the effect of the FDI attack and stabilize the output of DC hybrid Nanogrid. The twine-delayed actor-critic (TDAC) scheme, as an advanced reinforcement learning (RL), is developed for the adaptive design of the mitigation mechanism. The twin critic networks assess the quality of selected variables of mitigation mechanism, and the policy is updated by the actor network according to the evaluation. The training of deep neural networks (DNNs) of TDAC is realized in such a way that the effect of FDI threats disappears and stabilizes the DCHNG under CPLs simultaneously. Compared with the conventional cyber defense schemes which are developed based on the system model, the proposed technique doesn't need the dynamic model of the system. The real-time tests based on the Arduino Mega2560 setup are carried out and real-time tests of DCHNG reveal the effectiveness of the suggested scheme to address cybersecurity issues.

A modified multilevel LLC resonant inverter for multi-string photovoltaic (PV) applications. An efficient variable frequency and an integrated control method for the modulation index is proposed. To obtain the best efficiency, a steady output voltage gain, “ZVS, and a wide range of load variation, the designed LLC tank has been developed to run with a frequency range close to or at a resonant frequency. All the selected parameters for the analysis were emphasized in detail. Furthermore, the behaviour of the developed converter was investigated under varying load conditions at nominal input voltage, and under conditions of varying solar insolation and at full load. It was found that the control system responded to both changes successfully and maintained the value of the output voltage at its expected level by varying the switching frequency within a selected range, and produced 1000V,5kW with 97.6% efficiency. Also, the developed five-level converter differed in LLC resonant behaviour compared to its typical behaviour with conventional full/half-bridge converters, as the LLC RC operated below resonant frequency under all load and input conditions. The theoretical results were validated experimentally using a lab prototype and through simulation using MATLAB-SIMULINK. Selected findings were discussed to portray the efficacy of the designed converter.

The high-penetration of sustainable energy resources in the hybrid microgrids necessitates deploying the power electronic interface systems (e.g., rectifiers, inverters, and converters) for conversion purposes. However, the utilization of such technologies reduces the inertia of microgrids which highly threaten their stability. The stability challenges of microgrids are heightened when the phase-locked loop devices are installed in the converter-based systems. In this work, a fractional order disturbance-observer-based control (FO-DOBC) is developed for advanced virtual inertia control (AVIC) of microgrids with sustainable resources, electric vehicles, and storage units. In particular, the effect of phase-locked loop’s dynamics on the stability of microgrid is investigated. To dynamically respond to the disturbances in the microgrid and phase-locked loop’s dynamics, the coefficients embedded in the FO-DOBC are adaptively adjusted by the stochastic policy gradient clipping. By training the neural networks of SPGC, the FO-DOBC controller is designed in such a way that maximizes a reward function defined based on the system requirements. The comprehensive examinations based on the Arduino testbed are carried out to appraise the feasibility of the suggested virtual-based controller in a real-time framework. The real-time outcomes of the microgrid reveal that the AVIC based on FO-DOBC controller (designed by the stochastic policy gradient clipping) provides better responses than conventional virtual inertia control. Moreover, the suggested AVIC controller provides a higher level of stability against the reduction of inertia (between 1 % to 10 %) from its nominal value.

This paper introduces a new series of non-isolated boost DC-DC converters, showcasing a novel high step-up switching cell integrated into a basic boost converter to form a new transformerless converter design. The primary focus is on achieving significant voltage gains with moderate duty cycles while minimizing the stress on semiconductor devices. Comprehensive comparative analyses underline the new converter's advantages over existing models regarding voltage and current stresses, component count, and performance metrics. Additionally, the converter's design simplifies transistor driving by utilizing a single switch, enhancing operational ease. Detailed theoretical explanations are provided for continuous conduction mode (CCM), discontinuous mode (DCM), and boundary condition mode (BCM), including equations for voltages and currents. The proposed converter's effectiveness is examined through simulation and experimental validation. A 260-watt PV panel supplied by the proposed converter prototype demonstrates the converter's impressive boosting capability and high efficiency across 0.5 a duty cycle with a constant input voltage, providing solid proof of concept for the proposed designs. These findings promise substantial advancements in designing and applying high-gain boost converters in various electronic, industrial, and renewable energy applications.

The continual growth of renewables in the distribution scale is converting the conventional arrangement of power grid into a modern arrangement in which the Distribution Generation (DG) units are included within the distribution section of any power system. These renewable units include power electronics-based converters in their construction and the advent of these new technologies deteriorates the Power Quality (PQ) and introduces serious issues of harmonic distortion in both voltage and current waveforms. This research attempts to alleviate the problem of harmonic distortion in low voltage distribution networks containing solar PV modules through the proper sizing of adaptive Passive Harmonic Filters (PHFs). For this purpose, a real distribution network is simulated by ETAP environment, and the harmonic's load flow study is performed to specify the harmonic content at various busbars in the network. Hence PHF models based on the frequency scan performed are proposed to minimize the Total Harmonic Distortion (THD) in the network. The results of the simulations performed have shown that this approach reduces the THD significantly to comply with the permissible limits specified by the international standards IEEE STD 519 and IEC STD 61000-3-6.

Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

This paper presents a generalized formulation to determine the optimal operating strategy and cost optimization scheme for a MicroGrid.Prior to the optimization of the microgrid itself, the system model components from some real manufactural data are constructed.The proposed cost function takes into consideration the costs of the emissions NOx, SO 2 , and CO 2 as well as the operation and maintenance costs.The microgrid considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array , a fuel cell, and a battery storage.The optimization is aimed at minimizing the cost function of the system while constraining it to meet the customer demand and safety of the system.

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.

Electric vehicle (EV) drivers aim to charge their vehicles cost-effectively and with minimal charging time. Meanwhile, the ever-increasing number of EVs without charging control strategies could result in a massive surge in peak demand, potentially overloading distribution equipment and violating voltage constraints. To tackle this challenge, this paper introduces a transactive energy market (TEM) framework for an EV parking lot (EVPL) equipped with photovoltaic (PV) panels and battery systems (BSs), considering the preferences of both EV drivers and the EVPL operator. In this framework, EVs parked in the EVPL participate in the TEM by submitting their charging flexibility through response curves, which indicate the compensation required for different values of flexibility. Furthermore, the proposed model allows the EVPL operator to utilize the flexibility of EVs in the vehicle-to-grid (V2G) program by incentivizing EV drivers, considering their preferences and the degradation cost of EV batteries. The study employs the stochastic programming method to model uncertainties in PV output, electricity prices, and EV availability. It also incorporates BS degradation costs and carbon emissions constraints into the EVPL scheduling problem. Linearization techniques are then applied to transform the non-linear optimization problem into a mixed-integer linear programming (MILP) model. Finally, applying the model to a case study validates its superiority in satisfying the preferences of both EV drivers and EVPL.

This paper proposes a generalized formulation to determine the optimal operating strategy and cost optimization scheme as well as the reduction of the emissions for a MicroGrid (MG).Multiobjective (MO) optimization is applied to the environmental/economic problem of the MG.The proposed problem is formulated as a nonlinear constrained MO optimization problem.Prior to the optimization, models for system components from real data are constructed.The problem formulation takes into consideration the operation and maintenance costs as well as the emissions NOx, SO 2 , and CO 2 reduction.The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage.The optimization is aimed at minimizing the cost function of the system while constraining it to meet the costumer demand and safety of the system.We also add a daily income and outgo from sale or purchased power.The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions.The comparison with other techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.

Due to the high penetration of renewable energy sources (RES) such as wind units in power systems, the need to check the stability of transmission networks has been given more attention than before. Therefore, in this paper, due to the importance of the topic, the wide area damping controller (WADC) has been used for the battery energy storage system (BESS) connected to the photovoltaic unit and the permanent magnet synchronous generator (PMSG) in the dc link. The WADC design is based on free weight matrices (FWM), which can solve a set of constraints based on the linear matrix inequality (LMI) based on the delay-dependent feedback control theory. The working method is that the constraints related to LMI are considered in such a way that it has the ability to tolerate the maximum amount of time delay. FWM has been used to communicate between LMI constraints and the maximum value of the time delay margin. FWM matrices are also based on an iterative algorithm based on linearization of the conical complement, which tries to search for the most optimal value for the control parameters. To implement the simulation results in MATLAB software, an improved power system of 16 machines has been used, the results of which are clearly analyzed and show the superiority of the proposed method compared to other mentioned methods.

Power System Electromagnetic Transient (EMT) simulation is crucial for designing and evaluating power system control systems. It helps researchers optimize control strategie s and assess their effectiveness under different conditions. This paper introduces a detailed model of a 4 MW grid-following inverter PV model for verifying the response to faults and the overcurrent protection scheme. The grid-following inverter with Dynamic Reactive Control (DRC) is compared to a simplified fault response model. The study analyzes the impact of DRC current limiting model on protection blinding under different irradiance conditions. It highlights the significance of detailed PV inverter modeling in identifying and predicting DN blinding scenarios. The transient simulations are conducted on the IEEE 9-bus system with a 4 MW PV power plant in different scenarios. This provides a more comprehensive assessment and analysis of Inverter-Based Resources (IBRs) modeling and transient simulation.

This study tested four forecasting models combined with 3x3 SOM maps for predicting power quality parameters (PQPs) named decision tree (DT), KNN algorithm, bagging decision tree (BGDT), and boosting decision tree (BODT). The input variables used are weather conditions (air temperature, wind speed, air pressure, Ultraviolet, solar irradiance) with states of four types of home appliances (AC heating, light, fridge, TV) represented by one decimal number. Target Outputs are Power Voltage (U), total harmonic distortion of voltage (THD u ), total harmonic distortion of current (THD i ), power factor (PF), and power load (PL). The experiments were carried out in two stages: in the first stage, clustering dataset using self-organizing maps (SOM), 3x3 SOM in total nine hexagon nodes was used. In the second stage, inside each node builds four forecasting models: decision tree (DT), K-Nearest Neighbor(KNN) algorithm, bagging decision tree (BGDT), and boosting decision tree (BODT). Root Mean Square Error (RMSE) was used for evaluating the performance of studied models.

AbstractThe high penetration of distribution generators (DGs), such as photovoltaic (PV), has made optimal overcurrent coordination a major concern for power protection. In the literature, the conventional single or multi‐objective function (OF) for phase overcurrent relays (OCRs) scheme faces challenges in terms of stability, sensitivity, and selectivity to handle the integration of DGs and ground fault scenarios. In this work, a new optimal OCR coordination scheme has been developed as a multifunction scheme for phase and ground events using standard and non‐standard tripping characteristics. This research introduces and validates a coordinated optimum strategy based on two new optimization approaches, the Tug of War Optimization algorithm (TWO) and the Charged System Search algorithm (CSS), to mitigate the effects of DGs on fault currents and locations across the power network. Industrial software is used to create a case study of a CIGRE power network equipped with two 10 MW PV systems, and the results of the proposed new optimum coordination scheme are compared to traditional schemes. The findings show that the proposed multifunction OCR scheme is able to reduce the tripping time of OCRs over different fault and grid operation scenarios and increase the sensitivity of the relays in islanding operation mode.

Despite the enormous natural resources in Libya, the energy sector is facing serious challenges in sustainable development. Among the main areas of concern is Libya's current production which is more than 33 TWh of electrical energy to meet the demand of the local electricity needs. It is anticipated that the demand for energy will substantially increase in the near future. Consequently, the consumption of oil and gas reserves will peak, which would result in a reduction in the national economic revenue and more carbon dioxide emissions. To prevent the aforementioned consequences, Libya needs to search for alternative resources to meet some of its present and future energy needs. This paper reviews the prospects of solar energy as one of the major renewable energy sources available in Libya. Based on a documented survey of the energy status, this study reviews the national energy policy frameworks in developing countries; particularly, on how to overcome the ever-increasing energy crisis through utilizing solar energy. In addition, it focuses on the improvement of the current energy situation and resolving the lingering challenges facing the energy generation and supply in Libya. To conquer this, proper awareness and consideration should be regarded to the available technology of harnessing of the resources and urgently formulate, produce and adopt a renewable energy plan for the future energy generation.

This paper focuses only on the problem of voltage fall among the other power quality (PQ) problems. The electrical power grid of Alkufra was selected as a case study. Firstly, the required data of the studied network and that of a planned PV-grid-connected system in the same area were surveyed and collected. Secondly, the models of system components, implementation, and simulations were achieved with the help of using ETAP software. Finally, a comparison between seven scenarios, including base-case and feasible solutions, was investigated based on load flow analysis. The main findings reveal that in the base case, a voltage reduction at all buses of 11 kV was recorded. It was varied between 6.14% and 7.47%. By readjustment of the taps changer, coupling the PV plant, and switchable capacitor banks, a great improvement in voltage reduction was achieved through the seventh scenario. The voltage reduction was kept within a margin range not exceeding 3.98% at all 25 buses.

Meteorological factors such as solar irradiance and temperature have effects on the performance of grid-connected solar photovoltaic stations. In this study, the performance assessment of a 62.4 KWp grid-connected solar photovoltaic system installed in Tripoli-Libya has been carried out. The results presented were based on meteorological data measured in the site during 2018–2020 and recorded every 10 minutes. The impact of the environmental factors on the performance parameters has been investigated. The results presented in this study showed that the recorded sunshine hours range between 5.08 h per day in January and 11.79 h per day in July with an average of about 3048 h per year. The measured monthly averaged daily global solar radiation on horizontal surface varies between 3.07 and 7.87 $\text{kWh}/\mathrm{m}^{2}/\text{day}$ , and the total annual energy output predicted based on solar radiation measured at tilted surface $(32^{\circ})$ was 116.78 MWh/year.