Showing papers in "International Transactions on Electrical Energy Systems in 2022"

Analysis of Single-Diode PV Model and Optimized MPPT Model for Different Environmental Conditions

Abstract: The performance of photovoltaic (PV) systems must be predicted through accurate simulation designs before proceeding to a real-time application to avoid errors. However, predicting the cohesive relationship between current and voltage and estimating the parameters of a single diode model become a perplexing task due to insufficient data in the datasheet of PV panels. This research work presents single-diode solar PV system simulation analysis under different conditions, and the performance is improved by introducing an optimization-based maximum power point tracking (MPPT) strategy. Before simulation, a mathematical model for a single diode and optimization approaches are presented in this research work. Particle swarm optimization (PSO), genetic algorithm (GA), BAT optimization, and grey wolf optimization (GWO) model-based MPPT circuits are designed, and the performances are comparatively analyzed. The simulation results identify the nonlinear relationship between current and voltage and between power and voltage as characteristic curves for different temperature and irradiance values. For maximum power (Pmax), the maximum peak point tracking power and efficiency are analyzed to verify the optimization-based MPPT system. The simulation results demonstrate that the GWO model obtains a maximum tracking efficiency (TE) of 98%, which is much better than that of other optimization techniques.

A Modified Seven-Level Inverter with Inverted Sine Wave Carrier for PWM Control

Abstract: The conventional multilevel inverter necessitates more active switching devices and high dc-link voltages. To minimalize the employment of switching devices and dc-link voltages, a novel topology has been proposed. In this paper, a novel minimum switch multilevel inverter is established using six switches and two dc-link voltages in the proportion of 1 : 2. In addition, the proposed topology is proficient in making seven-level voltages by appropriate gate signals. The PWM signals were produced using several inverted sine carriers and a single trapezoidal reference. When compared to other existing inverters, this configuration needs fewer components, as well as fewer gate drives. Furthermore, this module can generate a negative level without the use of a supplementary circuit such as an H-Bridge. As a result, overall cost and complexity are greatly reduced. The proposed minimum switch multilevel inverter operation is validated through simulations followed by experimental results of a prototype.

Power Quality Improvement for Vehicle-to-Grid and Grid-to-Vehicle Technology in a Microgrid

Abstract: The demand for electric vehicles continues to grow, as evidenced by global sales of electric vehicles reaching 2.2 million in 2019 and more than doubling to 6.6 million in 2021. The rapid growth of renewable energies and electric vehicles (EVs) necessitates the use of microgrids, which are a promising solution to the problem of integrating large-scale renewables and EVs into the electric power system. Besides, the essential policy support provided by the government is an increase in the availability of public charging infrastructure for EVs. This research employs a fast-charging configuration of an off-board charger with DC energy transfer. Implementation of DC energy transfer for vehicle-to-grid and grid-to-vehicle technology in a microgrid due to DC charging’s unrestricted charger-rated power and rapid power transfer. However, the integration of EVs in the Microgrid system creates some operational challenges, which in this research are power quality issues such as harmonics in power systems that affect both utilities and consumers. The design models using the PI controller and the fuzzy controller based on MATLAB software are simulated to determine the control system’s effectiveness. These simulations assess the control system’s performance, and both approaches help improve the system’s performance power quality by minimizing the system’s total harmonic distortion (THD). According to the results, the fuzzy logic controller exceeded the traditional PI controller as demonstrated by minimizing the THD and also in terms of improving the waveform quality which achieved high accuracy with good performance. This research also utilized the fuzzy logic controller to control the power transfer between EVs and the microgrid, which differs from other research work, to achieve high system efficiency for the benefit of consumers.

An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance

Abstract: Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers’ different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy.

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Abstract: This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers’ parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods.

A Detailed Review of MMC Circuit Topologies and Modelling Issues

Abstract: MMC is a promising technology for MTDC systems and would transform into the concept of Supergrids in the near future. The salient features of MMC are modularity, reduced dv/dt and di/dt stress on switches, voltage and power scalability, inherent fault blocking capacity, transformerless operation, and improved power quality. However, there are some technical issues and challenges to be critically analysed and addressed. There is room for development of novel and enhanced MMC based on SM configurations to enable higher efficiency, improved power quality, compactness, and DC fault blocking capacity. Moreover, development of efficient and accurate models is required for the studies of MTDC grids during steady-state and transient conditions. Literature review suggests a need for studying and comparing different MMC modelling approaches because no modelling technique can be best suited for all applications. The main contribution of this paper is to provide a comprehensive review of recent developments in MMC in terms of SM configurations. This paper also presents an in-depth review of systematic comparison of different models of MMC, which can enable appropriate selection of model based on target studies and desired accuracy and efficiency. Finally, the associated research challenges and future trends are presented.

Voltage Quality Enhancement of Low-Voltage Smart Distribution System Using Robust and Optimized DVR Controllers: Application of the Harris Hawks Algorithm

Abstract: The voltage quality (VQ) index has become a significant measure of recent power system stability. The integration of photovoltaic (PV) systems plus smart home loads (SHLs) at low voltage levels (LVLs) has resulted in various issues such as harmonics rise and voltage instabilities as a result of faults and systems nonlinearity. In this work, a dynamic voltage resistor (DVR) is implemented to enhance VQ, and its dynamic performance hinges on its control system ability. To enhance the DVR’s control system, for surpassing nonstandard voltage with a quick response and harmonics reduction at LVL under harsh operating events, an optimal controller design using the Harris Hawks algorithm (HHA) is proposed. To verify the value of the suggested solution, the hard operating events (voltage sag, voltage swell, fluctuating voltage, and imbalanced voltage) are examined and assessed. To show the effectiveness of the HHA technique, a comparison of the % total harmonic distortion (THD) reductions achieved by the suggested and conventional controllers of DVR is conducted for the scenarios under study. Moreover, the suggested controller stability is analyzed and assessed using Lyapunov’s function. The benefits of the optimized controller system are inferred from the results, including their robustness, simplicity, efficient harmonic rejection, minimal tracking error, quick response, and sinusoidal reference track. The results of the simulation show that the DVR’s optimized controller is efficient and effective in maintaining a voltage at the needed level with low THD, safeguarding the sensitive load as expected, and showing a noticeable improvement in voltage waveforms. The mathematical modeling of HHA, PV system, DVR, and SHLs are all verified using MATLAB\Simulink.

Elimination of Harmonics in Multilevel Inverter Using Multi-Group Marine Predator Algorithm-Based Enhanced RNN

Abstract: Multilevel inverters (MLI) are becoming more common in different power applications, such as active filters, elective vehicle drives, and dc power sources. The Multi-Group Marine Predator Algorithm (MGMPA) is introduced in this study for resolving transcendental nonlinear equations utilizing an MLI in a selective harmonic elimination (SHE) approach. Its applicability and superiority over various SHE approaches utilized in recent research may be attributed to its high accuracy, high likelihood of convergence, and improved output voltage quality. For the entire modulation index, the optimum switching angles (SA) from Marine Predator Algorithm (MPA) is utilized to control a three-phase 11-level MLI employing cascaded H-bridge (CHB) architecture to regulate the vital element and eliminate the harmonics. The limitation of SHE is that it is difficult to find solutions for nonlinear equations. As a result, specific optimization approaches must be used. Artificial Intelligence (AI) algorithms can handle such a nonlinear transcendental equation successfully, although their time consumption as well as convergence abilities vary. Here, recurrent neural network (RNN) is considered where the hidden neurons are tuned by MGMPA with the intention of harmonic distortion parameter (HDP) minimization, thus called as enhanced recurrent neural network (ERNN). The method’s resilience and consistency are demonstrated by simulation and analytical findings. The MGMPA method is more effective and appropriate than various algorithms including the MPA, Harris Hawks optimization (HHO), and Whale optimization algorithm (WOA), according to simulation data.

Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems

Abstract: In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems.

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

Abstract: The optimal power flow issue (OPFI) can be solved in this work using the recently developed algorithm, gorilla troops algorithm (GTA). The goal of OPFI is to reduce numerous functions such as minimizing fuel costs, emissions, and power losses and improving the voltage stability related to electric power networks (EPNs). The GTA is inspired by gorillas’ social habits, which include migration to a strange region, migration toward a specified spot, traveling to other gorillas, competing for adult females, and escorting the silverback. The developed GTA is tested with and without the inclusion of the Thyristor-Controlled Series Capacitor (TCSC) devices in the system. The proposed GTA is applied on a practical Egyptian West Delta-EPN (WD-EPN) and the standard IEEE 57-bus EPN and with and without the inclusion of the TCSC devices to appraise the GTA algorithm’s performance in the OPFI. In addition, the proposed GTA is applied on a large-scale IEEE 118 bus system with higher outperformance compared to particle swarm optimization. The results illustrate that the fuel costs, emissions, voltage stability, and power losses are reduced for the standard IEEE 57-bus EPN with and without TCSC devices by a percentage of (18.847% and 18.818%), (59% and 58.97%), (13.405% and 11.507%), and (64.337% and 65.178%), respectively, while fuel costs, emissions, voltage stability, and power losses are reduced for WD-EPN with and without TCSC devices by a percentage of (8.547%, 8.565%), (13.641%, 13.6%), and (61.949%, 61.954%), respectively. A comparison study is conducted to demonstrate the GTA’s effectiveness when compared with other recently developed algorithms such as improved Salp Swarm Algorithm, quasi-reflection jellyfish search, Salp Swarm Algorithm, improved heap-based algorithm, bat search algorithm, social network search algorithm, electromagnetic field optimization, and other well-known algorithms as well. According to the comparison with these algorithms, the GTA demonstrates the best results among the attained results.

Design and Implementation of Hybrid PV/Battery-Based Improved Single-Ended Primary-Inductor Converter-Fed Hybrid Electric Vehicle

Abstract: In order to enhance the power transformation stage’s power transfer capabilities and efficiency, in this article, improved three-port two step-up single-ended primary-inductor converters (SEPIC) converter fed (Photovoltaic )PV- Hybrid Electric Vehicle was proposed. In comparison to the standard single-stage SEPIC, the proposed converter accepts a wider range of input voltages. The proposed three-port converter uses a multiple-winding high-frequency transformer (HFT) to integrate the dual sources and provide greater voltage gain with lesser elements. Furthermore, by predicting the drive torque need, the power management algorithm (PMA) included with the proposed PV-hybrid electric vehicle (HEV) minimizes the drive motor’s power consumption. An experimental model with a power output of 6 kW and a voltage range of 12 to 600 volts has been created and tested. The designed model has 94.11% efficiency.

Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks

Abstract: Every year, each solar panel suffers an efficiency loss of 0.5% to 1%. This degradation of solar panels arises due to environmental and electrical faults. A timely and accurate diagnosis of environmental faults reduces the damage caused by faults on the panel. In recent years, deep learning precisely convolutional neural networks have achieved wonderful results in many applications. This work is focused on finely tuning pretrained models of convolutional neural networks, especially AlexNet, GoogleNet, and SqueezeNet. Based on the performance metrics, SqueezeNet is used for training thermal images of solar panels and for the classification of environmental faults. The results obtained show that SqueezeNet has a significant testing accuracy of 99.74% and F1 score of 0.9818, which make the model successful in identifying environmental faults in solar panels and help users to protect the panels.

A Method for Parameter Identification of Distribution Network Equipment Based on Sequential Model-Based Optimization

Abstract: We present the first model-based parameter identification method in the power distribution network to successfully achieve parameter identification directly based on sequential model-based optimization. This method is building a model with a posteriori probability to optimize an objective function. Furthermore, to achieve an efficient exploration, three different acquisition functions, i.e., random search, tree-structured Parzen estimator approach, and simulated annealing, were proposed. We applied our three models and the conventional model-free method to the actual feeder data with no adjustment of the other conditions. The experiment shows that our method achieves at least 25% and 70% improvements in accuracy and convergence speed, respectively.

Optimal Location and Sizing of BESS for Performance Improvement of Distribution Systems with High DG Penetration

Abstract: This work proposes an optimal location and sizing of battery energy storage system (BESS) installation for performance improvement of distribution systems with high distributed generation (DG) penetration level where the DGs comprise photovoltaics (PV) and wind turbines (WT). The installation of the BESS can reduce costs incurred in the systems, alleviate reverse power flow when the systems are in the high DG penetration level, and also achieve peak shaving during high demand. To find the optimal location and sizing of the BESS, three optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), and salp swarm algorithm (SSA), are applied, and their performances are compared. The considered objective function is the system costs consisting of transmission loss cost, peak power cost, and voltage deviation cost. The system performance improvement is compared in terms of transmission loss, peak demand, and voltage regulation reductions. IEEE 33- and 69-bus distribution systems with high DG penetration are tested to investigate the performance improvement of the BESS installation. The results found that the installation of the BESS could successfully decrease system cost, improve voltage profile, reduce power losses, alleviate reverse power flow, and achieve peak shaving where PSO and SSA are found to be the best competitive algorithms. So, the proposed method can be further applied to find the optimal location and sizing of the BESS to improve the performance of practical systems in the future.

A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

Abstract: The concept of energy management in smart homes has received increasing attention in recent years, particularly on issues such as creating a balance between user privacy and reducing energy costs. Accordingly, this article proposes a user-oriented multi-objective approach, which minimizes energy costs and maximizes consumer privacy. In addition, a home energy management system is suggested for smart homes to optimize the energy consumption pattern of appliances. On the other hand, considering challenges in energy management of smart homes, the concept of demand-side management (DSM) is introduced. The objective of the proposed method is to reduce energy consumption to lower consumers’ electricity bills. Also, it improves user comfort (UC) in average waiting time conditions. In this research, a smart home equipped with an energy management system and smart home appliances that can inject electric power into the upstream network is considered the main system. This framework leads to a multi-objective optimization problem in which the two objectives mentioned above are considered two separate dimensions. To solve the problem, an ITS-BF Algorithm is used, which employs a random search to schedule home appliances and batteries based on the application of flexible devices in smart homes. The case studies show that the proposed method can considerably respect and satisfy users’ privacy and reduce the energy cost to an acceptable level. Finally, the numerical results obtained from the simulation have been analyzed to evaluate the proposed method’s efficiency. The simulation results show that an ITS-BF algorithm performs better than the existing methods in reducing costs and waiting time.

Developing a Marine Predator Algorithm for Optimal Power Flow Analysis considering Uncertainty of Renewable Energy Sources

Abstract: Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate.

Presenting of the Magnetic Coupling-Based Transformer-Less High Step-Up DC-DC Converter for Renewable Energy Applications

Abstract: This study presents a new high step-up converter based on the voltage multiplier cell and coupled inductor for renewable energy applications such as fuel cell and photovoltaic power systems. This converter achieves a high voltage conversion ratio using a coupled inductor and voltage multiplier cell (VMC). The voltage multiplier cell acts as a passive clamp circuit and reduces the maximum voltage across the power switch. The suggested topology has only one power switch in its structure, which leads to low cost and volume. The other benefits of the proposed structure are low components count, low input current ripple, low voltage stress throughout the semiconductors, high efficiency, zero-current switching (ZCS), and zero-voltage switching (ZVS) of diodes. Besides, due to the soft-switching condition of the diodes, the reverse recovery problem can be decreased. To show the effectiveness of the suggested topology, operation survey, steady-state analysis, and efficiency calculation are provided. Additionally, the comparison study with other similar converters illustrates the superiority of the proposed structure. Finally, an experimental prototype with 150 W rated power, 50 kHz switching frequency, and 24 V input voltage is implemented to prove the mathematical analysis and effectiveness of the proposed DC-DC converter.

Design and Development of Fixed-Frequency Double-Integral SM-Controlled Solar-Integrated Bidirectional Quasi Z-Source DC-DC Converter in Standalone Battery Connected System

Abstract: DC microgrids have been quite popular in recent times. The operational challenges like control and energy management of the renewable-driven standalone DC microgrids have been an interest of research. This paper presents a bidirectional quasi Z-source DC-DC converter (BQZSDC). This converter topology has been developed based on a conventional buck-boost type bidirectional converter, and it interfaces the storage system and the common DC bus. The challenge, however, lies in effectively managing the uncertain renewable energy sources and the storage system and catering for the loads simultaneously. An effective control strategy is needed for that energy management and to achieve various microgrid objectives. This paper deals with one such effective control strategy implemented for BQZSDC. That is, the fixed-frequency double-integral sliding mode control (FF-DISMC) controls the converter to regulate the DC bus voltage and battery current. A detailed analysis of the controller is conducted, and its performance is evaluated for both charging (buck) and discharging (boost) modes. Simulations have been performed in MATLAB, showing that the controller performs satisfactorily in achieving the objectives of voltage regulation and battery current regulation. Finally, the performance of the proposed controller is validated with the hardware setup.

An Efficient Design of Adaptive Model Predictive Controller for Load Frequency Control in Hybrid Power System

Abstract: The technology has proceeded so much that the power system should be substantial and explicit to give optimal results. Ever-increasing complexities of the power system and load disparity cause frequency fluctuations leading to efficiency degradation of the power system. In order to give a suitable real power output, the system entails an extremely perceptive control technique. Consequently, an advanced control method, that is, an adaptive model predictive controller (AMPC), is suggested for load frequency control (LFC) of the series power system which comprises photovoltaic (PV), wind, and thermal power. The suggested method is considered to enhance the power system execution as well as to decrease the oscillations due to a discrepancy in the system parameters and load disturbance under a multi-area power system network. The AMPC design verifies the constant frequency by maintaining a minimum steady state error under varying load conditions. The proposed control approach pledge that the steady-state error of frequencies and interchange of tie line powers is maintained in a given tolerance constraint. The effectiveness of the proposed controller is scrutinized by conventional controllers like genetic algorithm-tuned PI (GA-PI), firefly algorithm-tuned PI (FA-PI), and model predictive controller (MPC) to show the competence of the proposed method.

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

Abstract: As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

Operation Optimization of Integrated Energy System considering Power-to-Gas Technology and Carbon Trading

Abstract: Application of power-to-gas (P2G) technology can implement bidirectional energy flow between power network and gas network, which contribute to improved energy coupling, better operational flexibility, and high economic efficiency in the integrated energy system (IES). This study proposes a detailed IES model based on the refined P2G model according to the characteristics of P2G equipment. An optimal efficiency matching coefficient is proposed to increase the rate of energy equipment utilization. To address the carbon allocation problem, the carbon trading mechanism is employed in an optimization model with the consideration of economic benefits and costs (i.e., sales benefits, operating cost, carbon trading cost, wind power, and photovoltaic curtailment punishments). Case studies verify the advantage of the proposed optimization model. Furthermore, the results show that the P2G with gas tank mode has an obvious advantage in a comprehensive operating capability of IES.

Participation of Energy Storage-Based Flexible Hubs in Day-Ahead Reserve Regulation and Energy Markets Based on a Coordinated Energy Management Strategy

Abstract: In order to increase energy efficiency, the energy hub is considered as a form of aggregator and coordinator of various resources and storage. With the optimal performance of resources and storage generators based on a proper energy management system, it is expected that hubs can gain financial benefits from energy markets and ancillary services. So, the paper presents the participation of networked energy hubs in day-ahead (DA) reserve regulation and energy markets, where the hub operator incorporates a coordinated energy management (CEM) strategy to manage power sources and energy storage devices within the hub. Hence, this problem maximizes the total profit of hubs in the DA energy and up and down reserve markets. Also, the problem is constrained by optimal power flow (OPF) constraints in gas, electricity, and thermal networks, reserve limits, and hub constraints, including the model of the combined heat and power (CHP), renewable energy source (RES), electrical/thermal storage, parking lots of electric vehicles (EVs), and boiler. Following that, a linear format is obtained for the nonlinear equation using traditional linearization methods so that an optimal solution is found in less time considering less computational error. Eventually, a standard case system is used to test the strategy, and thus, the capabilities of the approach are investigated. The obtained findings validate the potential of the proposed design in enhancing the economic situation of power sources and storage in hub form, which can enhance operation indices by optimal management of the hub so that the energy management of resources and storage in the form of a hub based on CEM compared to their independent management plan has been able to increase the profit of these elements in energy and up and down reserve markets by about 17%, 28%, and 15%, respectively. Regarding technical indices of energy networks, the proposed scheme by creating low energy losses in the gas network and providing pressure drop, overvoltage, and overtemperature within their permissible limits succeeded in reducing the energy losses in electricity and heat networks by about 83% and 38%, respectively, compared to power flow studies. Also, in these conditions, it has reduced the maximum voltage and temperature drop by 45% and 39%, respectively.

A New Continuous Input Current Nonisolated Bidirectional Interleaved Buck-Boost DC-DC Converter

Abstract: In this paper, a new interleaved bidirectional buck-boost DC-DC converter is proposed. The input current of this converter is continuous and has a low ripple, that cause reduction in the size of the input filter of the converter. Because of these features, this converter is appropriate for renewable applications such as fuel cells and photovoltaic (PV) panels for obtaining maximum power in which the continuity of the input current is essential. The operation principle of this converter is detailed, and its power losses calculation shows the positive effects of the low input current ripple on its efficiency. The input current ripple of the proposed converter and conventional interleaved buck-boost converter has been calculated in detail. In addition, the comparison results of this converter with conventional interleaved buck-boost converters and other similar structures confirm that the proposed converter without utilizing extra components achieves continuous input current with low ripple. Compared with other buck-boost structures, the low input current ripple in the presented converter causes an improvement in its efficiency. An experimental prototype is implemented in the laboratory to confirm the correctness of theoretical analyses.

Design and Analysis of FOPID-Based Damping Controllers Using a Modified Grey Wolf Optimization Algorithm

Abstract: This study proposes a novel modified grey-wolf optimization algorithm (MGWOA) to enhance power system stability. The power system stabilizer and static synchronous series compensator (SSSC) are used as damping controllers. Additionally, fractional-order PID (FOPID) controller is used to handle the system nonlinearities and thus achieve better performance. The control parameters are tuned using the proposed MGWOA method which has been verified on unimodal and multimodal functions. Single-machine infinite bus (SMIB) and multimachine power system (MMPS) are taken as case studies to analyze the efficacy of the proposed controller. Minimization of rotor speed deviation is considered an objective function. The results obtained from the MGWOA-tuned FOPID-based damping controllers are compared with those obtained using recently developed efficient and competitive heuristic algorithms. It was observed that the MGWOA method is well-suited for damping low-frequency oscillations. Furthermore, statistical analysis is performed on the obtained results to justify the superiority of the MGWOA method. The simulation results suggest that the MGWOA exhibits superior performance characteristics when applied to a real power system.

Design and Implementation of Hybrid Transmission Line Protection Scheme Using Signal Processing Techniques

Abstract: A hybrid scheme for transmission line protection (HSTLP) using the Stockwell transform (ST), Wigner distribution function (WDF), and alienation coefficient (ACF) is designed. Current signals are analyzed using the ST, WDF, and ACF to compute the Stockwell fault index (SFI), Wigner fault index (WFI), and alienation coefficient fault index (ACFI), respectively. These fault indexes are used to derive a hybrid signal processing fault index (HSPFI), which is implemented for the detection of transmission line fault events. The peak magnitude of HSPFI is compared with a preset threshold magnitude (TH) to identify the fault. The statistical formulation is proposed for fault location on the power transmission line. Fault classification is achieved using the number of faulty phases. A hybrid ground fault index (HGFI) is used to recognize the involvement of the ground during the fault event. This HGFI is determined by processing zero sequence current using ST and WDF. The performance of algorithm is tested by various case studies for fault impedance variation, variable sampling frequency, fault incidence angle variation, reverse power flow on transmission line, highly loaded line, different fault locations online, and noisy conditions. The algorithm is also validated to detect a fault on a practical transmission line of large area utility grid of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in India. The algorithm performs better than the Hilbert–Huang transform (HHT)-based protection scheme and wavelet transform (WT)-based protection scheme available in the literature in terms of mean error of fault location, fault location accuracy, and noise level. The proposed protection scheme efficiently detected, classified, and located the faulty events such as single-phase-to-ground fault (SPGF), two-phase fault (TPF), two-phase-to-ground fault (TPGF), three-line fault (TLF), and three-line-to-ground fault (TLGF). Transmission line fault location accuracy of 99.031% is achieved. The algorithm performs well even with a high noise level of 10 dB SNR.

Interaction Mechanism and Coordinated Control of Commutation Failure Prevention in Multi-Infeed Ultra-HVDC System

Abstract: Commutation failure (CF) is an inherent drawback of line-commutated converter high-voltage DC (LCC-HVDC) system. And CF prevention (CFPREV) control is widely applied in UHVDC lines to mitigate the subsequent CF. However, the formation of a multi-infeed UHVDC system induces power exchange and voltage interaction between AC buses during the transient process, making the behaviors and mitigation of CFs more complex. Consequently, exploring the impact of CFPREV control on adjacent stations and optimizing the CFPREV control to adapt to the interaction characteristics in a multi-infeed system is necessary. Considering the interaction characteristics, the impact of CFPREV control on the commutation process of inverters in a multi-infeed system is analyzed through formula derivation and simulation verification. It is found that although CFPREV control can effectively mitigate the CFs of the inverter near the fault, it will further increase the risk of concurrent CF (CCF) of remote inverters due to the interaction on the AC side. To solve this problem, a coordinated control scheme of CFPREV controls in a multi-infeed UHVDC system is proposed. The output of the CFPREV control of the inverter near the fault can be adjusted adaptively according to the commutation margins of remote inverters. And the probability of CCF caused by CFPREV control is consequently decreased. Case studies are conducted based on Henan provincial multi-infeed UHVDC system in China to verify the analysis result and the effectiveness of the coordinated control scheme of CFPREV controls.

A Simplified Sine Cosine Algorithm for the Solution of Optimal Reactive Power Dispatch

Abstract: In this article, a simplified sine cosine algorithm (SSCA) is applied to solve the optimal reactive power dispatch (ORPD) issues by estimating the control variables. This algorithm uses sine cosine functions and generates number of random solutions to obtain the best solution by fluctuating inwards or outwards. The SSCA is implemented in the ORPD problem to find the best control variables to achieve minimum power loss and maximum net savings. Furthermore, the efficacy of SSCA is validated with other recently used algorithms considering three case studies, i.e., IEEE-30, -57, and -118 bus test system. The results show that the SSCA approach finds more precise and superior ORPD solutions. A comparison among SSCA and other methods proves the robustness of SSCA to attain the solution with faster convergence. The statistical analysis is performed to justify the effectiveness of SSCA by yielding minimum operating cost and maximum net savings as compared to other techniques considered in this study.

Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

Abstract: Early and accurate fault detection in electrical power grids is a very essential research area because of its positive influence on network stability and customer satisfaction. Although many electrical fault detection techniques have been introduced during the past decade, the existence of an effective and robust fault detection system is still rare in real-world applications. Moreover, one of the main challenges that delays the progress in this direction is the severe lack of reliable data for system validation. Therefore, this paper proposes a novel anomaly-based electrical fault detection system which is consistent with the concept of faults in the electrical power grids. It benefits from two phases prior to training phase, namely, data preprocessing and pretraining. While the data preprocessing phase executes all elementary operations on the raw data, the pretraining phase selects the optimal hyperparameters of the model using a particle swarm optimization (PSO)-based algorithm. Furthermore, the one-class support vector machines (OC-SVMs) and the principal component analysis (PCA) anomaly-based detection models are exploited to validate the proposed system on the VSB dataset which is a modern and realistic electrical fault detection dataset. Finally, the results are thoroughly discussed using several quantitative and statistical analyses. The experimental results confirm the effectiveness of the proposed system in improving the detection of electrical faults.

Integrating Industrial Appliances for Security Enhancement in Data Point Using SCADA Networks with Learning Algorithm

Abstract: The process of ensuring automatic operation for industrial appliances using both supervision and control techniques is a challenging task. Therefore, this article focuses on implementing Supervisory Control and Data Acquisition (SCADA) for controlling all industrial appliances. The design process of implementation case is performed using an analytical framework by examining the primary energy sources at the initial state; thus, a smart network is supported. The designed mathematical model is integrated with a learning technique that allocates resources at proper quantities. Further, the complex manual tuning of individual appliances is avoided in the projected method as the input variables are driven in a direct way at reduced loss state. In addition, the data processing state of individual appliances is carried out using central data controller where all parametric values are stored. In case any errors are observed, then SCADA network fixes the error in an automated way, reducing end-to-end delays in all appliances. To validate the effectiveness of the proposed method, five scenarios are examined and simulated where outcomes prove that SCADA network using learning models provides optimal results on an average of 84 percent as compared to the existing models without learning algorithm.

Probabilistic Quantification of Distribution System Resilience for an Extreme Event

Abstract: The occurrence of natural disasters such as cyclones, earthquakes, and floods has increased worldwide, and their effect is most intense in localized regions. These events expose weaknesses in the power system infrastructures and show how well-prepared the system is to operate its services in a resilient way. During and after an extreme event, the outage and service disruption happens due to the inability of the affected part of the grid to cope with disruptions, which leads to insufficient resilience of the system. It is becoming more critical to enhance the resilience of electrical networks to extreme weather occurrences through suitable hardening processes and smart operational techniques. A reliable prevention approach necessitates a quantitative resilience metric that can estimate the effects of the future extreme events on distribution systems and assess the possible benefits of various planning strategies. This study aims to address the issue of lack of resilience metrics and proposes probabilistic metrics for evaluating the performance of distribution systems resilience in case of extreme weather events. Specifically, this study introduces active and passive resilience concepts to provide insights into system response. The proposed resilience metrics for various weather scenarios are quantified for the IEEE 33-bus system. The simulation framework also analyzes the effects of different operational and structural resilience improvement approaches on the proposed resilience metrics.