Search Results (255)

With the rapid advancement of smart grids driven by renewable energy integration and the extensive deployment of supervisory control and data acquisition (SCADA) and phasor measurement units (PMUs), addressing the escalating alarm flooding via intelligent analysis of large-scale alarm data is pivotal to safeguarding the safe and stable operation of power grids. To tackle these challenges, this study introduces a pioneering alarm optimization framework based on symmetry-driven crowdsourced active learning and interpretable deep reinforcement learning (DRL). Firstly, an anomaly alarm annotation method integrating differentiated crowdsourcing and active learning is proposed to mitigate the inherent asymmetry in data distribution. Secondly, a symmetrically structured DRL-based hierarchical attention deep Q-network is designed with a dual-path encoder to balance the processing of multi-scale alarm features. Finally, a SHAP-driven interpretability framework is established, providing global and local attribution to enhance decision transparency. Experimental results on a real-world power alarm dataset demonstrate that the proposed method achieves a Fleiss’ Kappa of 0.82 in annotation consistency and an F1-Score of 0.95 in detection performance, significantly outperforming state-of-the-art baselines. Additionally, the false positive rate is reduced to 0.04, verifying the framework’s effectiveness in suppressing alarm flooding while maintaining high recall. Full article

This paper proposes a regional inertia estimation method in power systems using ambient data measured by phasor measurement units (PMUs). The proposed method employs band-pass filtering to suppress the low-frequency influence of mechanical power and to attenuate high-frequency noise and discrepancies between rotor speed and electrical frequency. By utilizing a simple first-order AutoRegressive Moving Average with eXogenous input (ARMAX) model, this process allows the inertia constant to be directly identified. This method requires no prior model order selection, rotor speed estimation, or computation of the rate of change of frequency (RoCoF). The proposed method was validated through simulation on three benchmark systems: the Kundur two-area system, the IEEE Australian simplified 14-generator system, and the IEEE 39-bus system. The method achieved area-level inertia estimates within approximately ±5% error across all test cases, exhibiting consistent performance despite variations in disturbance models and system configurations. The estimation also maintained stable performance with short data windows of a few minutes, demonstrating its suitability for near real-time monitoring applications. Full article

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

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid interactions and often lack labelled examples. To address this, the present work introduces a unique, frequency-centric framework for unsupervised detection and root cause analysis of grid anomalies using high-resolution micro-Phasor Measurement Unit ($\mu$PMU) data. Unlike previous studies that focus primarily on voltage phasors or rely on predefined event labels, this work employs SpectralNet, a deep spectral clustering approach, integrated with autoencoder-based feature learning to model the nonlinear interactions between frequency, ROCOF, voltage, and current. These methods are particularly effective for unexpected frequency variations because they learn intrinsic, hidden structures directly from the data and can group abnormal frequency behavior without prior knowledge of event types. The proposed model autonomously identifies distinct root causes such as unbalanced loads, phase-specific faults, and phase imbalances behind hazardous frequency deviations. Experimental validation on a real solar-integrated distribution feeder in the UK demonstrates that the framework achieves superior cluster compactness and interpretability compared to traditional methods like K-Means, GMM, and Fuzzy C-Means. The findings highlight SpectralNet’s capability to uncover subtle, nonlinear patterns in $\mu$PMU data, offering an adaptive, data-driven tool for enhancing grid stability and situational awareness in renewable-rich power systems. Full article

The proliferation of photovoltaic energy in the electricity grid presents a significant challenge in terms of management, control, and optimization, especially due to its dependence on weather behavior and cloud passing. Even if there are a great number of articles centered on study cloud passing effects, such as voltage flickers, voltage fluctuations, or ramping events, the approaches are quite heterogeneous and lack a broader perspective. A key factor might be the limiting data sets, as wide power generation data sets often omit meteorological data and vice versa. This study uses an advanced monitoring system based on phasor measurement units (PMUs), developed by the authors. The monitoring system is installed at a photovoltaic plant and generates high-quality synchronous irradiance and power data, enabling the joint analysis of irradiance transients, solar power ramp rates, and voltage fluctuations. Therefore, the results of this article present a detailed analysis of the production parameters of photovoltaic plants, focusing on the effects of passing clouds on the photovoltaic plant’s power, current, and voltage. To that end, compression algorithms such as the Swinging Door Algorithm (SDA), commonly used to detect ramp events, were employed. It was found that SDA produces a similar ramp rate output with power and irradiance data, suggesting that both data sets may be complementary. In addition, voltage fluctuations attributable to passing clouds were analyzed. Full article

This paper presents a weakly supervised learning framework for real-world event identification in transmission networks using phasor measurement unit (PMU) data. The growing integration of renewable energy sources has introduced greater variability in grid conditions, intensifying the need for accurate event detection. Although high-resolution PMU measurements enable event identification to be formulated as a classification problem, traditional supervised learning approaches are hindered by the scarcity of labeled data, and acquiring large-scale, high-quality labeled PMU datasets remains prohibitively expensive. To overcome this challenge, we propose an automated PMU data-labeling method that combines domain knowledge with machine learning techniques through the use of labeling functions. A novel t-cherry junction tree-based estimation algorithm is introduced to enhance label accuracy, and a greedy strategy is employed to reduce computational complexity. These components are integrated into a weakly supervised framework capable of training robust event classifiers using limited labeled data and abundant unlabeled data. Extensive experiments on real-world PMU datasets demonstrate that our approach achieves competitive accuracy with significantly fewer labeled samples compared to conventional data-driven methods, highlighting its adaptability and resilience under real-world conditions. Full article

In the process of decarbonizing electricity generation, renewable energy sources are actively being integrated into traditional power systems. As a result, the inertia of the energy system is reduced, and the speed of transition processes is accelerated. This can lead to instability under small disturbances. This necessitates changing traditional approaches to implementing algorithms for emergency control automation. The paper proposes a methodology to solve the problem of small-signal stability analysis in low-inertia energy systems. The task of the small-signal stability analysis problem is reduced to multi-class classification problems. The proposed methodology can be divided into two main parts: selecting the most informative input features and classifying control actions. The IEEE24 mathematical model of the power system serves as a data source. Measurements from this model are received via phasor measurement units. Among the feature selection algorithms considered, the Random Forest algorithm proved to be the most effective. In terms of efficiency in solving the control action selection problem, the LightGBM algorithm proved dominant. Its accuracy in noise-free data was 98%. With 20 dB of data noise, the algorithm’s accuracy decreased slightly: 97%. The algorithm’s time delay was only 0.07 ms. Full article

The development of new tools to assist the system operator has been crucial in modern power systems due to the system complexity and operational challenges. Among these tools, the system’s load margin, which indicates the maximum load level allowed without instability occurring, stands out. The physical characteristics of the modern power system in the stability threshold condition and the abundant data from Phasor Measurement Units (PMUs) can be used by machine learning techniques to predict the load margins of power systems. This paper proposes a new Physics-Informed Neural Network for computing the precise value of the load margin of power systems equipped with PMUs adopting experimental and physical knowledge in the training process through three loss functions. A PMU allocation procedure is applied to reduce the number of PINN entries. Case studies applying the proposed PINN are performed on the IEEE 68-bus system, and comparative analyses are conducted with traditional Artificial Neural Networks (ANNs), Graph Neural Networks (GNNs) and Physics-Guided Neural Networks (PGNNs). Results show better Root Mean Square Error values for the proposed PINN compared to the ANN, GNN and PGNN for different numbers of PMUs allocated in the test system. Full article

Recent technological advances in communication networks, intelligent devices, power electronics, and phasor measurement units have significantly transformed the operation of modern power systems. This evolution gave rise to smart grids, which enable the flow of real-time information on the operational state of the grid and of control commands across multiple communication infrastructures, using a variety of protocols and standards, between control centers and devices deployed throughout the grid’s physical structure. At the same time, it has exposed power systems to new challenges and threats, due to the vulnerabilities inherited from the different components they integrate. Attackers have a variety of attacks at their disposal, by which they can disturb the availability of electricity as well as cause damage to the smart grid’s physical structure. Therefore, cybersecurity has become an important aspect of the smart grid concept. This field of research has attracted the attention of many researchers, and in the last decade or so, the number of studies on the cyber-physical security of smart grids has surged significantly. Proportionally, an important number of survey papers were published as well. It has therefore become more difficult to navigate literature on the topic of smart grid cyber-physical security due to the large number of papers, the complexity of the grid’s structure, and the variety of attacks, resolution methods, and techniques. To address this issue, in this work, we present a comprehensive review of existing literature reviews on the topic of smart grid cyber-physical security. We reviewed 100 survey papers, which were categorized into general surveys, attack-specific surveys, method-specific surveys, and component-specific surveys. We discussed and highlighted research tendencies in terms of attacks and methods used to protect smart grids. Additionally, we presented an overview of the different research challenges and possible future directions. Full article

To further enhance the deployment efficiency of synchronous phasor measurement units and rationally select deployment configuration schemes, an improved configuration method based on integer programming algorithms is proposed. Based on the existing deployment method of integer programming algorithm, on the one hand, the special conditions of end nodes and zero injection nodes are taken into consideration. By analyzing the corresponding node model matrix, special nodes are given priority processing, achieving the condition simplification of the algorithm model. On the other hand, the evaluation indicators for the construction of schemes with the same minimum deployment quantity in the solution set obtained from the iterative solution of the algorithm are further analyzed and compared, so as to screen out a more reasonable deployment method. After conducting simulation tests on the IEEE-14, IEEE-30, and NE-39 power node systems using the MATLAB platform, the depth-first search algorithm and the improved simulated annealing algorithm were compared with the improved method. Eventually, this method had fewer deployments in a similar number of deployments and less deployment time in a similar number of deployments. The results verified the superiority of this method in terms of time and deployment quantity for PMU deployment problems. Full article

Unbalanced active distribution networks must be carefully analyzed to minimize undesirable implications from internal unbalances and the incorporation of intermittent sources, such as DG (Distributed Generation). A coordinated voltage regulation mechanism is being created employing a MAS (Multi-Agent System) control structure to solve the difficulties mentioned earlier. The proposed technique increases coordination between DGs and Shunt capacitors (SCs) to optimize the voltage profile and reduce overall power losses, along with voltage and current unbalanced factors in the proposed unbalanced 3-phase radial distribution network. To ensure improved real-time monitoring, PMUs (Phasor Measurement Units) measure the state parameters of the above-regulated distribution network in realtime. Because it does not necessitate the placement of PMUs at all nodes for total network observability, it is a cost-effective technique for estimating network state. The IEEE standard, a 25-bus unbalanced 3-phase distribution network feeder, is used to assess the viability of the recommended technique. MATLAB R2024a programming is used to simulate the case studies. Full article

Poorly damped low-frequency oscillation modes can destabilize power systems in the event of contingencies. Advances in the widespread use of phasor measurement units (PMUs) in power systems have led to the development of wide-area damping controllers (WADCs) capable of ensuring good damping ratios for these oscillation modes. However, cyberattacks or communication failures affect PMU data and can cause WADC malfunctions. Poor WADC operation can even destabilize the power system. Therefore, this paper proposes the design of a WADC robust to communication failures through a multi-objective optimization model requiring high damping ratios for the closed-loop system and the existence of a symmetric and positive defined matrix to guarantee the stability of the system. Bio-inspired algorithms can solve this proposed multi-objective optimization model, and the stellar oscillation optimizer proved to be a bio-inspired algorithm with an excellent ability to reach an optimal solution. Case studies show that defining the limiting values of the WADC time constants and the existence of this symmetric, positive-definite matrix are beneficial for good system dynamic performance. Full article

Low-frequency oscillation modes are studied in small-signal angular stability because, if not adequately damped, they can cause power system instability in the event of a contingency. The interconnection and expansion of large power systems has led to the emergence of multiple local and inter-area modes and required new damping control strategies for these modes. The expansion of the use of Phasor Measurement Units in power systems has led to the development of new control strategies such as Wide-Area Damping Controllers (WADCs) that use data from PMUs to dampen low-frequency oscillations. Although the benefits of WADCs are promising, there are challenges in designing a WADC. This paper proposes a two-stage algorithm for the robust design of a WADC for modern power systems. The first stage consists of solving an optimization model and finding the WADC parameters that maximize the damping ratios of all modes of the linearized system model for a set of operating points. The second stage consists of refining the WADC parameters through an iterative algorithm. Cases were studied for a set of IEEE 68-bus operating points through modal analysis and time-domain simulations. The results obtained demonstrated the good performance of the proposed two-stage algorithm compared with an existing WADC design method based on a Linear Quadratic Regulator. Full article

This paper presents an in-depth review of cybersecurity challenges and advanced solutions in modern power-generation systems, with particular emphasis on smart grids. It examines vulnerabilities in devices such as smart meters (SMs), Phasor Measurement Units (PMUs), and Remote Terminal Units (RTUs) to cyberattacks, including False Data Injection Attacks (FDIAs), Denial of Service (DoS), and Replay Attacks (RAs). The study evaluates cutting-edge detection and mitigation techniques, such as Cluster Partition, Fuzzy Broad Learning System (CP-BLS), multimodal deep learning, and autoencoder models, achieving detection accuracies of (up to 99.99%) for FDIA identification. It explores critical aspects of power generation, including resource assessment, environmental and climatic factors, policy and regulatory frameworks, grid and storage integration, and geopolitical and social dimensions. The paper also addresses the transmission and distribution (T&D) system, emphasizing the role of smart-grid technologies and advanced energy-routing strategies that leverage Artificial Neural Networks (ANNs), Generative Adversarial Networks (GANs), and game-theoretic approaches to optimize energy flows and enhance grid stability. Future research directions include high-resolution forecasting, adaptive optimization, and the integration of quantum–AI methods to improve scalability, reliability, and resilience. Full article

Electric vehicle (EV) charging has become a key factor in grid integration, impact analysis, and the development of intelligent charging strategies. However, the rapid rise in EV adoption poses challenges for charging infrastructure and grid stability due to the inherently variable and uncertain charging behavior. Limited access to high-resolution, location-specific data further hinders accurate modeling, emphasizing the need for reliable, privacy-preserving tools to forecast EV-related grid impacts. This study introduces a comprehensive methodology to emulate real-world EV charging behavior using a real-time simulation environment. A physics-based EV charger model was developed on the Typhoon HIL platform, incorporating detailed electrical dynamics and control logic representative of commercial chargers. Simulation outputs, including active power consumption and state-of-charge evolution, were validated against field data captured via phasor measurement units, showing strong alignment across all charging phases, including SOC-dependent current transitions. Quantitative validation yielded an MAE of 0.14 and an RMSE of 0.36, confirming the model’s high accuracy. The study also reflects practical BMS strategies, such as early charging termination near 97% SOC to preserve battery health. Overall, the proposed real-time framework provides a high-fidelity platform for analyzing grid-integrated EV behavior, testing smart charging controls, and enabling digital twin development for next-generation electric mobility. Full article