Search Results (82)

In modern power systems, distributed generation (DG) clusters such as wind and solar resources are increasingly being integrated into active distribution networks through DG cluster control, which enhances the economic efficiency and adaptability of the DGs. However, cyber attacks on cyber–physical systems (CPS) may disable control links within the DG cluster, leading to the loss of control over slave DGs and resulting in power deficits, thereby threatening system stability. Existing CPS security assessment methods have limited capacity to capture cross-domain propagation effects caused by cyber attacks and lack a comprehensive evaluation framework from the attacker’s perspective. This paper establishes a CPS system model and control–communication framework and then analyzes the cyber–physical interaction characteristics under DG cluster control. A logical model of cyber attack strategies targeting DG cluster inverters is proposed. Based on the control topology and master–slave logic, a probabilistic failure model for DG cluster control is developed. By considering power deficits at cluster point of common coupling (PCC) and results in internal network of the DG cluster, a physical consequence quantification method is introduced. Finally, a cyber risk assessment method is proposed for DG cluster control under cyber attacks. Simulation results validate the effectiveness of the proposed method. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

In distributed microgrid systems, inverters serve as the core components when distributed generation (DG) modules are integrated into the grid. Traditional inverters typically employ droop control; however, they lack damping and inertia mechanisms. Consequently, fluctuations in the grid frequency and voltage occur when system loads change, leading to a suboptimal power distribution. To address these limitations, this paper introduces an adaptive strategy into conventional droop control. Based on an adaptive algorithm, the real and reactive power are dynamically computed. Through coordinate transformation, decoupled control, and adaptive frequency compensation, the inverter’s output frequency and voltage are effectively regulated. By adjusting the reference current in a dual-loop control scheme, the active and reactive power distribution is optimized. Additionally, an improved adaptive algorithm is developed to compute the inverter’s AC frequency compensation, enabling the self-adaptive adjustment of the PI controller’s output. This facilitates frequency compensation in droop control, ensuring that the inverter’s output current and voltage remain synchronized with the grid phase, thereby enhancing grid stability during connection. Finally, the feasibility of the proposed algorithm is validated through Simulink simulations. Full article

Due to the increasing availability of new energy sources, the adaptability of traditional fault analysis and calculation methods has declined when applied to distribution networks. The reason is that the traditional ideal voltage source model cannot accurately reflect the impact of new energy from the main grid side on distribution networks. Moreover, the existing calculation methods fail to consider the influence of new energy on both the grid side and the distribution network side simultaneously, resulting in relatively large calculation errors and inaccurate fault characteristics. To address the above problems, this paper first studies the control strategy and current output characteristics of typical inverter-based resources (IBR) and establishes an integrated source model for the grid side with a high proportion of IBRs during faults. The model employs a parallel connection of an ideal voltage source with series impedance and a voltage-controlled current source. A model parameter identification method is proposed, leveraging a genetic algorithm and utilizing the normal operating electrical quantities at the port. Then, a fault-equivalent model and an iterative method for calculating electrical quantities in distribution networks are proposed, based on the integrated grid-side model. The method takes into account both distributed generators (DGs) and IBRs on the grid side, using the voltage error at the point of common coupling (PCC) as the convergence criterion for the iterative calculation. The simulation results of PSCAD/EMTDC show that the proposed model and calculation method have high accuracy. The model precisely reflects the characteristics of reduced port voltage and limited current during faults on the grid side. The amplitude errors of the electrical quantities are within 1%, and the phase angle errors are within 4°. Full article

The distribution network line has the risk of an unsuccessful three-phase blind reclosing in permanent fault. Based on the response of the inverter of the distributed generation (DG) to the short-term low-frequency voltage disturbance to the line to be detected, this paper proposes a non-fault identification method for the distribution network before three-phase reclosing, based on model parameter identification. During the disturbance period, when there is no fault after the arc is extinguished, the detection line is three-phase symmetrical, and each phase-to-ground loop is its own loop resistance and inductance linear network, which is independent of the fault location, transition resistance and other factors. Furthermore, the R–L network without fault is used as the identification reference model, and the least squares algorithm is used to identify the resistance and inductance parameters of each phase loop of the detection line by using the voltage and current response information of the line side during the excitation period so as to identify the fault state. The non-fault criterion before three-phase reclosing, characterized by the difference between the calculated value of resistance and inductance and the corresponding actual value, is designed. Finally, PSCAD is used to build a distribution network with DG for verification, and simulations under different fault locations and transition resistances are carried out. The results show that when the line is in a non-fault state, the parameter identification results of the three phase-to-ground circuits are highly consistent with the true value; that is, the non-fault state is determined. When the fault continues, there is a large deviation between the parameter identification results of at least one phase-to-ground loop and the corresponding real value, which does not meet the condition of the non-fault criterion. The method in this paper is more sensitive than the detection method using response voltage. Moreover, it is not necessary to add additional disturbance sources, which is expected to improve the economy and feasibility of three-phase adaptive reclosing applications for distribution lines with a large number of DGs. Full article

This paper discusses the operation and control of a low-voltage DC (LVDC) isolated distribution network powered by distributed generation (DG) from a variable-speed wind turbine induction generator (WTIG) to supply unbalanced AC loads. The system incorporates a DC-DC storage converter to regulate network voltages and interconnect battery energy storage with the DC network. The wind turbines are equipped with a squirrel cage induction generator (IG) to connect a DC network via individual power inverters (WTIG inverters). Loads are unbalanced ACs and are interfaced using transformerless power inverters, referred to as load inverters. The DC-DC converter is equipped with a novel control strategy, utilizing a droop regulator for the DC voltage to stabilize network operation. The control system is modeled based on Clark and Park transformations and is developed for the load inverters to provide balanced AC voltage despite unbalanced load conditions. The system employs the perturbation and observation (P&O) method for maximum power point tracking (MPPT) to optimize wind energy utilization, while blade angle controllers maintain generator performance within rated power and speed limits under high wind conditions. System operation is analyzed under two scenarios: normal operation with varying wind speeds and the effects of load variations. Simulation results using PSCAD/EMTDC demonstrate that the proposed LVDC isolated distribution network (DC) achieves a stable DC bus voltage within ±5% of the nominal value, efficiently delivers balanced AC voltages with unbalanced levels below 2%, and operates with over 90% wind energy utilization during varying wind speeds, confirming LVDC network reliability and robustness. Full article

This paper presents a comprehensive study on the dynamic modeling of distribution power systems with a focus on the integration of renewable energy sources (RESs) for stability analysis. Our research delves into the static and dynamic behavior of distribution systems, emphasizing the need for enhanced load modeling to mitigate planning and operational uncertainties. Using MATLAB/Simulink^®, we simulate four distinct study cases characterized by varying load types and levels of distributed generation (DG), particularly solar PV, under both balanced and unbalanced conditions. Our findings highlight the critical role of DG in influencing voltage stability, revealing that deviations in voltage and current during grid imbalances remain within acceptable limits. The study underscores the importance of DG-based inverters in maintaining grid stability through reactive power support and sets the stage for future research on microgrid simulations and battery storage integration to further enhance system stability and performance. Full article

DNA is continuously exposed to a variety of harmful factors, which, on the one hand, can force undesirable processes such as ageing, carcinogenesis and mutagenesis, while on the other hand, can accelerate evolutionary changes. Of all the canonical nucleosides, 2′-deoxyguanosine (dG) exhibits the lowest ionization potential, making it particularly prone to the one-electron oxidizing process. The most abundant type of nucleobase damage is constituted by 7,8-dihydro-8-oxo-2′-deoxyguanosine (^OXOdG), with an oxidation potential that is 0.56 V lower than that of canonical dG. All this has led to ^OXOdG, as an isolated lesion, being perceived as a sink for radical cations in the genome. In this paper, a comparative analysis of the electronic properties of an ^OXOGC base pair within the context of a clustered DNA lesion (CDL) has been conducted. It is based on previous DFT studies that were carried out at the M06-2x/6-31++G** level of theory in non-equilibrated and equilibrated condensed phases. The results of the comparative analysis presented here reveal the following: (A) The ionization potentials of ^OXOG₄C₂ were largely unaffected by a second lesion. (B) The positive charge and spin were found predominantly on the ^OXOG₄C₂ moiety. (C) The electron-hole transfers A₃T₃→G₄C₂ and G₄C₂←A₅T₁ were found in the Marcus inverted region and were resistant to the presence of a second DNA lesion in close proximity. It can therefore be reasonably postulated that ^OXOGC becomes the sink for a radical cation migrating through the double helix, irrespective of the presence of other 2′-deoxyguanosine lesions in the CDL structure. Full article

This paper proposes a sophisticated, fault-tolerant, and centralized secondary controller that is designed for inverter-based, islanded microgrids. The proposed controller enhances system resilience to unexpected network partitions, which typically occur due to the tripping of protective devices under fault conditions. In typical radially configured MGs, a line fault can cause protective devices to isolate the faulted line, thereby splitting the MG into two electrically independent sub-microgrids (SMGs), while retaining the existing communication and control framework. In contrast to traditional centralized and distributed secondary controllers, which often fail to restore the frequency to the nominal value (50 Hz) in split SMGs, the proposed controller exhibits exceptional performance. Through simulation studies on 6-bus and 13-bus islanded MG setups, the controller has not only demonstrated its ability to swiftly restore the nominal frequency in both SMGs within a few seconds (specifically 5 s), but also to ensure fair power distribution among the distributed generators (DGs) supplying the SMGs. This rapid frequency stabilization underscores the controller’s effectiveness in maintaining stable frequency levels immediately following a fault. In contrast, the use of traditional centralized and consensus controllers typically results in a frequency deviation of about 3 Hz from the nominal value in one of the SMGs during the microgrid’s partition. Full article

In islanded operation, precise power sharing is an immensely critical challenge when there are different line impedance values among the different-rated inverters connected to the same electrical network. Issues in power sharing and voltage compensation at the point of common coupling, as well as the reverse circulating current between inverters, are problems in existing control strategies for parallel-connected inverters if mismatched line impedances are not addressed. Therefore, this study aims to develop an improved decentralized controller for good power sharing with voltage compensation using the predictive control scheme and circulating current minimization between the inverters’ current flow. The controller was developed based on adaptive virtual impedance (AVI) control, combined with finite control set–model predictive control (FCS-MPC). The AVI was used for the generation of reference voltage, which responded to the parameters from the virtual impedance loop control to be the input to the FCS-MPC for a faster tracking response and to have minimum tracking error for better pulse-width modulation generation in the space-vector form. As a result, the circulating current was maintained at below 5% and the inverters were able to share an equal power based on the load required. At the end, the performance of the AVI-based control scheme was compared with those of the conventional and static-virtual-impedance-based methods, which have also been tested in simulation using MATLAB/Simulink software 2021a version. The comparison results show that the AVI FCS MPC give 5% error compared to SVI at 10% and conventional PI at 20%, in which AVI is able to minimize the circulating current when mismatch impedance is applied to the DGs. Full article

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities. Full article

This paper focuses on the comprehensive restoration of both voltage and frequency in a standalone microgrid (SAMG). In a SAMG, the power balance is achieved through traditional methods such as droop control for power sharing among distributed generators (DGs). However, when such microgrids (MGs) are subjected to perturbations coming from stochastic renewables, the frequency and voltage parameters deviate from their specified values. In this paper, a novel hybrid-type consensus-based distributed controller is proposed for voltage and frequency restoration. Data-based communication is ensured among the DGs for controlling voltage and frequency parameters. Different parameters such as voltage, frequency, and active and reactive power converge successfully to their nominal values using the proposed algorithms, thereby ensuring smooth operation of inverter-dominated DGs. Additionally, the machine-learning-based long short-term memory (LSTM) algorithm is implemented for renewable power forecasting using historical data from the proposed location for visualising the insolation profile. The effectiveness of our approach is demonstrated through a SAMG, which consists of four inverters, showing that the proposed approach can improve system stability, increase efficiency and reliability, and reduce costs compared to traditional methods. The complete study is performed in Python and MATLAB environments. Our results highlight the potential of data-driven approaches to revolutionise power system operation and control. Full article

Active islanding detection techniques majorly affect power quality due to injected harmonic signals, whereas passive methods have a large non-detection zone (NDZ). This article presents a new method based on the resultant sequential impedance component (RSIC) as a new approach to island detection with zero NDZs. The abrupt variable in the conventional impedance approach was replaced by the RSIC of the inverter in this method. When the measured value exceeds the threshold range, islanding is detected by monitoring the variations in the RSIC at the point of common coupling (PCC). For proper power utilization in the identified islands, a priority-based load-shedding strategy is also recommended and implemented in this article. Its efficacy was verified in a wide range of real-world settings. It offers superior stability in various non-islanding (NIS) scenarios to prevent accidental tripping. The proposed method advantages include a cheap cost, the simplicity of implementation, independence from the number and type of distributed generation (DG) units connected, and no power quality effects. Compared to other methods reported in the literature, the obtained detection times illustrate that the proposed method is superior. Full article

This paper presents a strategy to evaluate the performances of converter stations under the optimized operating points of hybrid AC-DC power systems with a reduced number of DC link variables. Compared to previous works reported with five DC-side control variables (CVs), the uniqueness of the presented optimal power flow (OPF) formulation lies within the selection of only two DC-side control variables (CVs), such as the inverter voltage and current in the DC link, apart from the conventional AC-side variables. Previous research has mainly been focused on optimizing hybrid power system performance through OPF-based formulations, but has mostly ignored the associated converter performances. Hence, in this study, converter performance, in terms of ripple and harmonics in DC voltage and AC current and the utilization of the converter infrastructure, is evaluated. The minimization of active power loss is taken as an objective function, and the problem is solved for a modified IEEE 30 bus system using a recently developed and very efficient Archimedes optimization algorithm (AOA). Case studies are performed to assess the efficacy of the presented OPF model in power systems, as well as converter performance. Furthermore, the results are extended to assess the applicability of the proposed model to the allocation of photovoltaic (PV)-type distributed generations (DGs) in hybrid AC-DC systems. The average improvement in power loss is found to be around 7.5% compared to the reported results. Furthermore, an approximate 10% improvement in converter power factor and an approximate 50% reduction in ripple factor are achieved. Full article