Search Results (20)

This work presents a lightweight and interpretable framework for the early warning of voltage stability degradation in distribution networks, based on fractal and spectral features from flow measurements. We propose a Fast Voltage Stability Index (FVSI), which combines four independent indicators: the Detrended Fluctuation Analysis (DFA) exponent α (a proxy for long-term correlation), the width of the multifractal spectrum Δα, the slope of the spectral density β in the low-frequency range, and the $c_2$ curvature of multiscale structure functions. The indicators are calculated in sliding windows on per-node series of voltage in per unit Vpu and reactive power Q, standardized against an adaptive rolling/first-N baseline, and anomalies over time are accumulated using the Exponentially Weighted Moving Average (EWMA) and Cumulative SUM (CUSUM). A full online pipeline is implemented with robust preprocessing, automatic scaling, thresholding, and visualizations at the system level with an overview and heat maps and at the node level and panel graphs. Based on the standard IEEE 13-node scheme, we demonstrate that the Fractal Voltage Stability Index (FVSI_Fr) responds sensitively before reaching limit states by increasing α, widening Δα, a more negative $c_2$, and increasing β, locating the most vulnerable nodes and intervals. The approach is of low computational complexity, robust to noise and gaps, and compatible with real-time Phasor Measurement Unit (PMU)/Supervisory Control and Data Acquisition (SCADA) streams. The results suggest that FVSI_Fr is a useful operational signal for preventive actions (Q-support, load management/Photovoltaic System (PV)). Future work includes the calibration of weights and thresholds based on data and validation based on long field series. Full article

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

One of the crucial types of information needed to guarantee the secure operation of power systems is their voltage stability condition. This is particularly true for power systems operating at peak hours or under abnormal conditions, such as contingencies. The literature shows several methods for voltage stability assessment; however, they are either accurate and computationally burdensome or less accurate and computationally efficient. The main goal of this research work is to propose methods that are both accurate and fast, features that are especially important in strict real-time operating conditions. Two new methods for computing the maximum loadability and the voltage stability margin of power systems are proposed. Both methods use a powerful, second-order, and non-divergent power flow with an optimally computed step size; however, each of them is initialized differently. Very high-quality initializations are obtained by using a linear voltage stability index and sensitivity analysis factors. This combination leads to a fast, robust, and accurate method, suited for strict real-time power system operation. The proposed methods require 90% fewer power flow runs compared with conventional methods, such as the continuation method for small systems, and tend to require even fewer power flow runs for larger systems. Computer simulations of the proposed methods use small benchmarks to large realistic power systems, showing that the requirements for real-time use—namely accuracy, robustness, and computational efficiency—are met. Full article

This research presents a novel methodology based on data analysis for improving voltage stability in transmission systems. The proposal aims to determine a single distributed generator’s optimal location and sizing using the Fast Voltage Stability Index (FVSI) as the primary metric under $N-1$ contingency conditions. The developed strategy systematically identifies the most critical transmission lines close to instability through a frequency analysis of the FVSI in the base case and across multiple contingency scenarios. Subsequently, the weak buses associated with the most critical line are determined, on which critical load increases are simulated. The Distributed Generator (DG) sizing and location parameters are then optimized through a statistical analysis of the inflection point and the rate of change of the FVSI statistical parameters. The methodology is validated in three case studies: IEEE systems with 14, 30, and 118 buses, demonstrating its scalability and effectiveness. The results show significant reductions in FVSI values and notable improvements in voltage profiles under stress and contingency conditions. For example, in the 30-bus IEEE system, the average FVSI for all contingency scenarios was reduced by 26% after applying the optimal solution. At the same time, the voltage profiles even exceeded those of the base case. This strategy represents a significant contribution, as it is capable of improving the stability of the electrical power system in all $N-1$ contingency scenarios with overload at critical nodes. Using a single DG as a low-cost and highly effective corrective measure, the proposed approach outperforms conventional solutions through statistical analysis and a data-centric approach. Full article

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller’s reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. Full article

Renewable and non-renewable energy harvesting and its storage are important components of our everyday economic processes. Lithium-ion batteries (LIBs), with their rechargeable features, high open-circuit voltage, and potential large energy capacities, are one of the ideal alternatives for addressing that endeavor. Despite their widespread use, improving LIBs’ performance, such as increasing energy density demand, stability, and safety, remains a significant problem. The anode is an important component in LIBs and determines battery performance. To achieve high-performance batteries, anode subsystems must have a high capacity for ion intercalation/adsorption, high efficiency during charging and discharging operations, minimal reactivity to the electrolyte, excellent cyclability, and non-toxic operation. Group IV elements (Si, Ge, and Sn), transition-metal oxides, nitrides, sulfides, and transition-metal carbonates have all been tested as LIB anode materials. However, these materials have low rate capability due to weak conductivity, dismal cyclability, and fast capacity fading owing to large volume expansion and severe electrode collapse during the cycle operations. Contrarily, carbon nanostructures (1D, 2D, and 3D) have the potential to be employed as anode materials for LIBs due to their large buffer space and Li-ion conductivity. However, their capacity is limited. Blending these two material types to create a conductive and flexible carbon supporting nanocomposite framework as an anode material for LIBs is regarded as one of the most beneficial techniques for improving stability, conductivity, and capacity. This review begins with a quick overview of LIB operations and performance measurement indexes. It then examines the recently reported synthesis methods of carbon-based nanostructured materials and the effects of their properties on high-performance anode materials for LIBs. These include composites made of 1D, 2D, and 3D nanocarbon structures and much higher Li storage-capacity nanostructured compounds (metals, transitional metal oxides, transition-metal sulfides, and other inorganic materials). The strategies employed to improve anode performance by leveraging the intrinsic features of individual constituents and their structural designs are examined. The review concludes with a summary and an outlook for future advancements in this research field. Full article

In the present work, a strategic multi-scenario EV ultra-fast charging station (CS) planning framework is designed to provide advantages to charging station owners, Distribution Network Operators, and EV owners. Locations of CSs are identified using zonal division and the Voltage Stability Index strategy. The number of chargers is determined using the Harris Hawk Optimization (HHO) technique while minimizing the installation, operational costs of CS, and energy loss costs considering all the power system security constraints. To ensure a realistic planning model, uncertainties in EV charging behavior and electricity prices are managed through the 2m-Point Estimate Method. This method produces multiple scenarios of uncertain parameters, which effectively represent the actual dataset, thereby facilitating comprehensive multi-scenario planning. This study incorporates annual EV and system load growth in a long-term planning model of ten years, ensuring the distribution network meets future demand for sustainable transportation infrastructure. The proposed research work is tested on a 33-bus distribution network and a 51-bus real Indian distribution network. To evaluate the financial and environmental benefits of the planning, a cost-benefit analysis in terms of the Return-on-Investment index and a carbon emission analysis are performed, respectively. Furthermore, to prove the efficacy of the HHO technique, the results are compared with several existing algorithms. Full article

This paper proposes a novel methodology to improve stability in a transmission system under critical conditions of operation when additional loads that take the system to the verge of stability are placed in weak bus bars according to the fast voltage stability index (FVSI). This paper employs the Newton–Raphson method to calculate power flows accurately and, based on that information, correctly calculate the FVSI for every transmission line. First, the weakest transmission line is identified by considering $N-1$ contingencies for the disconnection of transmission lines, and then all weak nodes associated with this transmission line are identified. Following this, critical scenarios generated by stochastically placed loads that will take the system to the verge of instability will be placed on the identified weak nodes. Then, the methodology will optimally size and place a single static VAR compensator SVC in the system to take the transmission system to the conditions before the additional loads are connected. Finally, the methodology will be validated by testing the system for critical contingencies when any transmission line associated with the weak nodes is disconnected. As a result, this paper’s methodology found a single SVC that will improve the system’s stability and voltage profiles to similar values when the additional loads are not connected and even before contingencies occur. The methodology is validated on three transmission systems: IEEE 14, 30, and 118 bus bars. Full article

Identifying the weak buses in power system networks is crucial for planning and operation since most generators operate close to their operating limits, resulting in generator failures. This work aims to identify the critical/weak node and reduce the system’s power loss. The line stability index ($L_{mn}$) and fast voltage stability index (FVSI) were used to identify the critical node and lines close to instability in the power system networks. Enhanced particle swarm optimization (EPSO) was chosen because of its ability to communicate with better individuals, making it more efficient to obtain a prominent solution. EPSO and other PSO variants minimized the system’s actual/real losses. Nodes 8 and 14 were identified as the critical nodes of the IEEE 9 and 14 bus systems, respectively. The power loss of the IEEE 9 bus system was reduced from 9.842 MW to 7.543 MW, and for the IEEE 14 bus system, the loss was reduced from 13.775 MW of the base case to 12.253 MW for EPSO. EPSO gives a better active power loss reduction and improves the node’s voltage profile than other PSO variants and algorithms in the literature. This suggests the feasibility and suitability of EPSO to improve the grid voltage quality. Full article

An electrical power system (EPS) is subject to unexpected events that might cause the outage of elements such as transformers, generators, and transmission lines. For this reason, the EPS should be able to withstand the failure of one of these elements without changing its operational characteristics; this operativity functionality is called $N-1$ contingency. This paper proposes a methodology for the optimal location and sizing of a parallel static Var compensator (SVC) in an EPS to reestablish the stability conditions of the system before $N-1$ contingencies take place. The system’s stability is analyzed using the fast voltage stability index (FVSI) criterion, and the optimal SVC is determined by also considering the lowest possible cost. This research considers $N-1$ contingencies involving the disconnection of transmission lines. Then, the methodology analyzes every scenario in which a transmission line is disconnected. For every one of them, the algorithm finds the weakest transmission line by comparing FVSI values (the higher the FVSI, the closer the transmission line is to instability); afterward, when the weakest line is selected, by brute force, an SVC with values of 5 Mvar to 100 Mvars in steps of 5 Mvar is applied to the sending bus bar of this transmission line. Then, the SVC value capable of reestablishing each line’s FVSI to its pre-contingency value while also reestablishing each bus-bar’s voltage profile and having the lowest cost is selected as the optimal solution. The proposed methodology was tested on IEEE 14, 30, and 118 bus bars as case studies and was capable of reestablishing the FVSI in each contingency to its value prior to the outage, which indicates that the algorithm performs with 100% accuracy. Additionally, voltage profiles were also reestablished to their pre-contingency values, and in some cases, they were even higher than the original values. Finally, these results were achieved with a single solution for a unique SVC located in one bus bar that is capable of reestablishing operational conditions under all possible contingency scenarios. Full article

The reliability of the electronic converter is a vital concern in an industrialized area. Capacitors are critical in electronic converters and are more likely to fail than other electronic gears. Due to aging, the capacitor progressively loses its original quality and capacitance, and the equivalent series resistance escalates. Hence, condition monitoring is a fundamental procedure for evaluating capacitor health that affords prognostic repairs to guarantee stability in power networks. The ESR and capacitance of the capacitor are commonly employed to estimate the condition grade. This study proposes an estimation scheme that utilizes the source current to assess the health condition of an aluminum capacitor. Several advanced intelligence techniques are adopted to estimate the parameters of an AEC in a three-phase inverter system. First, different signals used as inputs, such as input power, capacitor current, voltage, and power, output current, voltage, and power, are analyzed using fast Fourier transform and discrete wavelet transform analysis. Then, various indexes of the analyzed signals, such as RMS, average, median, and variance, are used as the inputs in learning models to monitor the AEC’s parameters. In addition, various input signals are combined to obtain the best combinations for capacitor monitoring. The estimated results prove that utilizing the source current combined with selected indexes improves the monitoring accuracy of the AEC’s health status. Full article

This paper presents a novel methodology for the calculation of angular indexes of an electrical system from stationary analysis, using load flow and nose curves (P–V) in each of the buses of the system to perform control actions and preserve or improve voltage stability. The control actions are proposed considering a novel method based on the concepts of the cutset angle (CA) and center of angle (COA). The target is a fast estimation of voltage-stability margins through an appropriate angular characterization of the whole system and for each load bus with a complete network and N-1 contingency criteria. The most significant enhancement is that the angular characterization is based on the COA, which is related to the angular dynamics of the system, and indirectly reflects the inertia and the respective angles of the generator rotor, as well as the impact on the angular equivalent-system model. Simulations showed that the COA is an important index to determine the location of occurrence of the events. The COA can also help aim where control actions, like the amount of load shedding, should be carried out to remedy the voltage problems. The proposed method is assessed and tested in the benchmark IEEE 39-bus system. Full article

The increase in demand and generator reaching reactive power limits may operate the power system in stressed conditions leading to voltage instability. Thus, the voltage stability assessment is essential for estimating the loadability margin of the power system. The grid operators urgently need a voltage stability assessment (VSA) method with high accuracy, fast response speed, and good scalability. The static VSA problem is defined as a regression problem. Moreover, an artificial neural network is constructed for online assessment of the regression problem. Firstly, the training sample set is obtained through scene simulation, power flow calculation, and local voltage stability index calculation; then, the class imbalance problem of the training samples is solved by the random under-sampling bagging (RUSBagging) method. Then, the mapping relationship between each feature and voltage stability is obtained by an artificial neural network. Finally, taking the modified IEEE39 node system as an example, by setting up four groups of methods for comparison, it is verified that the proposed method has a relatively ideal modeling speed and high accuracy, and can meet the requirements of power system voltage stability assessment. Full article

Increasing the proportion of renewable energy generations in remote island power systems is becoming essential for realizing decarbonized society. However, since inverter-connected renewable energies have different generation characteristics from conventional generators, the massive penetration can adversely affect system stability. In particular, fault events in such weak remote systems can cause fast voltage collapse, and there is a need to assess dynamic voltage stability. This study attempts dynamic voltage stability assessment using the critical boundary index (CBI) and investigates the impact of the virtual synchronous generator (VSG) on dynamic voltage stability. A remote island power system and VSG are modeled, and time-domain simulations are conducted with case studies of fault events. The simulation results show the potential of CBI to use for dynamic voltage stability assessment. Furthermore, the VSG can provide suitable power output during fault events and improve dynamic voltage stability. Full article

Intermittent power generated from renewable distributed energy resource (DER) can create voltage stability problems in the system during peak power production in the low demand period. Thus, the existing standard for operation and management of the distribution system limits the penetration level of the DER and the amount of load in a power system. In this standard, the hosting capacity of the DER is limited to each feeder at a level where the voltage problem does not occur. South Korea applied this standard, thereby making it hard to achieve its DER target. However, by analyzing the voltage stability of an integrated system, the hosting capacity of DER can be increased. Therefore, in this study, the maximum hosting capacity of DER is determined by analyzing an integrated transmission and distribution system. Moreover, the fast voltage stability index (FVSI) is used to verify the determined hosting capacity of DER. For this, the existing interconnection standard of DER at a feeder, distribution system, and transmission system level is investigated. Subsequently, a Monte Carlo simulation is performed to determine the maximum penetration of the DER at a feeder level, while varying the load according to the standard test system in South Korea. The actual load generation profile is used to simulate system conditions in order to determine the maximum DER hosting capacity. Full article