Power system security is one of the most considerable studies to understand the vulnerability of various contingencies occurring on the system. In this paper, the contingency ranking is performed for single-line outages. Among the increments of the load variation for cases like only active power loading, only reactive power loading, and both the active and reactive power loadings are considered to create various scenarios using the Active Power Performance Index (APPI). These scenarios have been classified through a Support Vector Machine Classifier (SVMC) to observe the impacts of line outage. The data has been generated using MATLAB software for the UPSEB Indian Utility 75-bus system. Python programming is used to classify using SVMC.

ABSTRACT The increasing frequency of extreme cold waves exacerbates wind power uncertainty, intensifying the trade‐off between robustness and economy in high wind penetration power systems. To address the problem, this paper proposes a DRO method based on distributionally robust Bayesian inference (DRBI). An ambiguity set defined by the Wasserstein metric is first constructed utilising historical wind data. Secondly, the likelihood distribution of wind power output is predicted using an XGB‐transformer model. To accurately characterise wind power output during cold waves, a posterior distribution is then constructed using the proposed DRBI framework. Next, a DRO dispatch model is constructed to ensure operational robustness while minimising total operating cost. Constraints include power balance, wind power uncertainty and system security requirements. The model is solved based on strong duality theory. Finally, the model is validated on a regional 30‐bus system and a modified IEEE 118‐bus system. Experimental results show that, compared to stochastic optimisation and robust optimisation models, the proposed model effectively balances robustness and economy under cold waves. Besides, accounting for wind power uncertainty, experimental results suggest maintaining wind power penetration at 10–20%. Moreover, the economic efficiency of the optimal schedule can be further improved by adjusting the sample size of cold‐wave scenarios.

Strategic deployment of FACTS devices for enhanced security in multi-area power systems

The rapid integration of wind power plants (WPPs) into modern electrical power systems (MEPSs) is crucial to global decarbonization, but it introduces significant technical challenges. Variability, intermittency, and forecasting uncertainty compromise frequency stability, voltage regulation, and grid reliability, particularly at high levels of renewable energy integration. To address these issues, adaptive control strategies have been proposed at the turbine, plant, and system levels, including reinforcement learning-based optimization, cooperative plant-level dispatch, and hybrid energy schemes with battery energy storage systems (BESS). At the same time, interoperability frameworks based on international standards, notably IEC 61850 and IEC 61400-25, provide the communication backbone for vendor-independent coordination; however, their application remains largely limited to monitoring and protection, rather than holistic adaptive operation. Real-Time Automation Controllers (RTACs) emerge as promising platforms for unifying monitoring, operation, and protection functions, but their deployment in large-scale WPPs remains underexplored. Validation of these frameworks is still dominated by simulation-only studies, while real-time digital simulation (RTDS) and hardware-in-the-loop (HIL) environments have only recently begun to bridge the gap between theory and practice. This review consolidates advances in adaptive control, interoperability, and validation, identifies critical gaps, including limited PCC-level integration, underutilization of IEC standards, and insufficient cyber–physical resilience, and outlines future research directions. Emphasis is placed on holistic adaptive frameworks, IEC–RTAC integration, digital twin–HIL environments, and AI-enabled adaptive methods with embedded cybersecurity. By synthesizing these perspectives, the review highlights pathways toward resilient, secure, and standards-compliant renewable power systems that can support the transition to a low-carbon future.

With the development of commercial complexes, higher demands are being placed on the stability and security of internal power systems. As a core component of distribution systems, high-voltage transformers play critical roles in energy conversion, power distribution, and fault isolation and protection. Meanwhile, the power consumption environments of critical users have become increasingly complex, characterized by diversified load types and frequently changing operating conditions, which present greater challenges to the reliable operation of high-voltage transformers. In this context, relying solely on a single signal source for fault diagnosis is no longer sufficient to meet practical requirements. Therefore, the introduction of multi-modal information fusion technology has become essential for improving diagnostic accuracy and comprehensiveness. To enhance the accuracy and intelligence of fault diagnosis in high-voltage transformers, this study proposes a multi-source information fusion-based fault diagnosis method based on the Short-Time Fourier Transform (STFT), Residual Network (ResNet18), and Bidirectional Gated Recurrent Unit (BiGRU), termed STFT-ResBiGRUNet. The proposed model utilizes fundamental electrical and environmental parameters, such as three-phase current, voltage, active power, reactive power, temperature, and humidity, as input features. By integrating the Efficient Channel Attention (ECA) mechanism, key feature representations are enhanced. Moreover, the model combines local feature extraction with the modeling of global temporal dependencies, thereby enabling efficient fault diagnosis of high-voltage transformers. Experimental results demonstrate that the proposed model exhibits significant robustness and high accuracy in noisy environments, outperforming state-of-the-art classification models.

Abstract Policies promoting a carbon-neutral economy are accelerating the energy transition towards renewable electricity generation, primarily through small-scale investments within distribution networks. As penetration of distributed generation increases, transmission system operators encounter increasing challenges in predictability. This study presents a coordination approach between transmission and distribution network operators in Türkiye, highlighting an advanced solar generation forecasting method to enhance secure network operations. In the initial phase, a centralized database was established to catalog distributed generation capacity and plant specifications. Web-based applications and services were developed to integrate distribution company assets into the transmission network model. By September 2024, over 30,000 power plants (99% photovoltaic, 17.8 GW total) had been integrated. Distribution companies are expected to supply real-time generation data to the transmission operator, though issues such as measurement errors and communication problems may limit data accuracy and continuity. Thereafter, a short-term solar forecasting system was developed to support intraday and day-ahead congestion analysis. The system provides high-resolution forecasts at the individual plant level for more than 20,000 locations. Machine learning algorithms were used for large-scale plants, while small-scale plant forecasts were based on meteorological data and physical modeling. Because accurate forecasting requires detailed plant parameters, a Particle Swarm Optimization algorithm was implemented to calibrate photovoltaic models. This approach enables accurate parameter estimation even when on-site data is limited, improving the performance and reliability of solar generation forecasts. This paper provides valuable insights into the coordination between transmission system operators and distribution system operators, emphasizing the importance of data integration and forecasting accuracy in managing distributed solar generation. Furthermore, it details the outcomes of an advanced solar generation forecasting system, demonstrating its effectiveness in improving predictability and supporting secure and efficient power system operations.

Accurate probabilistic load forecasting is essential for secure power system operation and efficient energy management, particularly under increasing renewable integration and demand-side complexity. However, traditional forecasting methods often struggle with issues such as non-linearity, non-stationarity, feature redundancy, and quantile crossing, which hinder reliable uncertainty quantification. To overcome these challenges, this study proposes a hybrid probabilistic load forecasting framework that integrates empirical mode decomposition (EMD), LASSO-based feature selection, and a monotone composite quantile regression neural network (MCQRNN) enhanced with kernel density estimation (KDE). First, EMD decomposes the raw load series into intrinsic mode functions and a trend component to mitigate non-stationarity. Then, LASSO selects the most informative features from both the decomposed components and the original time series, effectively reducing dimensionality and multicollinearity. Subsequently, the proposed MCQRNN model generates multiple quantiles under monotonicity constraints, eliminating quantile crossing and improving multi-quantile coherence through a composite loss function. Finally, Gaussian kernel density estimation reconstructs a continuous probability density function from the predicted quantiles, enabling full distributional forecasting. The framework is evaluated on two public datasets—GEFCom2014 and ISO New England—using point, interval, and density evaluation metrics. Experimental results demonstrate that the proposed EMD–LASSO–MCQRNN–KDE model outperforms benchmark approaches in both point and probabilistic forecasting, providing a robust and interpretable solution for uncertainty-aware grid operation and energy planning.

Cyber-physical power systems integrate sensing, communication, and control, ensuring power system resiliency and security, particularly in clustered networked microgrids. Software-Defined Networking (SDN) provides a suitable foundation by centralizing policy, enforcing traffic isolation, and adopting a deny-by-default policy in which only explicitly authorized flows are admitted. This paper proposes and experimentally validates a cyber-physical architecture that couples three DC microgrids through an SDN backbone to deliver rapid, reliable, and secure power sharing under highly dynamic conditions, including pulsed-load disturbances. The cyber layer comprises four SDN switches that establish dedicated paths for protection messages, supervisory control commands, and high-rate sensor data streams. An OpenFlow controller administers flow-rule priorities, link monitoring, and automatic failover to preserve control command paths during disturbances and communication faults. Resiliency is further assessed by subjecting the network to a deliberate denial-of-service (DoS) attack, where deny-by-default policies prevent unauthorized traffic while maintaining essential control flows. Performance is quantified through packet captures, which include end-to-end delay, jitter, and packet loss percentage, alongside synchronized electrical measurements from high-resolution instrumentation. Results show that SDN-enforced paths, combined with coordinated multi-microgrid control, maintain accurate power sharing. A validated, hardware testbed demonstration substantiates a scalable, co-designed communication-and-control framework for next-generation cyber-physical DC multi-microgrid deployments.

Abstract—Voltage stability is a critical aspect of maintaining the reliable operation of modern power systems, particularly with the increasing integration of renewable energy sources, dynamic loads, and complex grid configurations. This paper presents a comprehensive analysis of voltage stability assessment techniques aimed at enhancing the overall stability and resilience of power systems. Various methodologies, including continuation power flow, modal analysis, and time-domain simulation, are explored to evaluate system performance under different operating conditions. The study emphasizes the identification of weak buses, critical voltage margins, and potential collapse points to aid in preventive control strategies. Furthermore, the role of advanced computational intelligence methods such as Artificial Neural Networks (ANN), Fuzzy Logic, and Machine Learning algorithms in improving predictive accuracy and real-time monitoring is discussed. Comparative results demonstrate the efficiency of hybrid assessment models in detecting instability precursors and optimizing reactive power compensation. The findings contribute to the development of more robust voltage stability frameworks, ensuring secure and efficient power system operation in the evolving energy landscape.

The integration of high-penetration distributed renewable energy sources into new power systems introduces significant challenges, particularly frequent reverse power flows that threaten substation security. To address this issue, this paper proposes a novel safety assessment method based on a system dynamics (SD) framework. This approach uniquely emphasizes the critical roles of electrical interconnections among substation equipment and the fluctuations in distributed power output. The methodology involves analyzing operational characteristics to establish equipment correlations, developing a comprehensive fault probability function for each equipment by integrating multi-dimensional monitoring data and fault propagation factors, and constructing a system dynamics model using an adjacency matrix to represent operational relationships. The effectiveness of the proposed method is validated through a case study on a regional substation. Results demonstrate its capability to dynamically and accurately evaluate both equipment-level and system-wide safety status under reverse power flow conditions, providing a robust tool for enhancing the security and resilience of modern power systems.

Leveraging Multi-Task Learning for multi-label power system security assessment

ABSTRACT The economical, stable and efficient operation of power systems is intrinsically linked to secondary systems and their diverse functional applications. With the advancement of communication technologies, control algorithms, and emerging regulatory entities, modern power systems have evolved to become more intelligent yet complex compared to traditional grids. As the conceptual framework of power system security expands from primary system safety to a holistic cyber‐physical paradigm, the functional safety of secondary systems has emerged as a critical and paramount requirement for ensuring overall grid stability and reliability. This paper systematically reviews research on functional safety analysis and mitigation strategies for secondary systems from a cyber‐physical perspective. Firstly, the basic concept and propagation mechanism of power system functional security are introduced; secondly, the research difficulties of functional security are analysed from the three dimensions of system, data and software security; then, the current research status of functional security in different dimensions is summarised; finally, we offer a forward‐looking perspective on future research directions for analysing and mitigating functional safety risks, emphasising the prevention of physical grid failures caused by secondary system anomalies from a cyber‐physical viewpoint.

ICCK Transactions on Electric Power Networks and Systems is an academic journal devoted to research and further advancement of knowledge on phenomena related to electric power systems that connect electricity generation with consumers through transmission and distribution networks. Specifically, this journal will deal with the modeling and analyses of various problems related to the generation, transmission, distribution, consumption and trade of electricity. The concepts of electric power system security, safety, stability, flexibility, reliability, restoration, resilience, planning, operation, control and protection will be interrelated and considered by authors from different perspectives. The journal seeks to publish original, innovative and high-quality research papers that provide links between theory and applications, addressing the challenges of maintaining and improving the performance parameters of electric power networks and systems, as well as their components and devices. The papers addressing various research gaps in the fields of renewables, electric vehicles, drives, energy storage, power electronics, electricity market and wireless electricity transmission using analytical methods, numerical techniques, statistics, meta-heuristics and artificial intelligence are also welcome. The editors invite original research papers, reviews, letters, technical reports and case studies dealing with normal, optimal, emergency and fault operating regimes of electric power networks and systems. All submissions will undergo a fair and rigorous peer-review process to ensure the highest quality of publications. This journal aims to be a valuable source of information for scientists, researchers, industry professionals, and engineers working in an increasingly knowledge-based, interconnected and technology-driven world.

Power quality disturbances (PQDs) pose significant challenges in modern energy power plants-based systems (MEPPBS), especially with the increasing integration of renewable energy sources (RESs) such as wind and solar photovoltaic (PV) systems. The intermittent nature of these sources introduces voltage fluctuations, harmonics, and transient disturbances, affecting grid stability and reliability. This paper presents a novel dual algorithm-based protection approach for detecting, classifying, and mitigating PQDs in grid-connected MEPPBS. The proposed method utilized an advanced adaptive median filter (AMF) as a signal processing-state observer and a support vector machine (SVM)-based scheme to accurately identify disturbances, including voltage sags, swells, harmonics, interruptions, and transients. The proposed dual-algorithm approach, combining AMF and SVM, offers a novel solution that enhances PQD detection accuracy and speed compared to existing methods. Furthermore, the proposed scheme tests five individual PQDs and ten combined disturbances-based datasets, including voltage sags, swells, harmonics, interruptions, and transients were used to train the proposed SVM classifier. Then, SVM-based residuals were calculated by SVM algorithms from estimated AMF data, SVMBR index reveals the detection of PQDs quickly. Simulations were performed in MATLAB® R2022b (Version 9.13) to evaluate the effectiveness of the suggested approach under various operating conditions. The results demonstrate high detection accuracy of 97%, fast response times of less than 15 milliseconds, & robustness in discriminating different PQDs when trained by just 50% of the dataset under a signal-to-noise ratio (SNR) of 20dB. The proposed method achieves 96% precision, 94% recall, and a 0.04 false positive rate, demonstrating high accuracy and reliability in PQDs detection. The findings highlight the potential of the presented method to enhance power system resilience and ensure reliable operation in renewable energy-integrated grids.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-19149-6.

Secure and scalable power system event identification with renewable integration via federated LSTM and adaptive privacy mechanisms

The present study investigated factors influencing cybersecurity in railway critical infrastructure by identifying relevant factors and criteria and then prioritizing them in order of importance. To address the lack of multi-criteria analysis in previous studies on this topic, the present study applied the analytical hierarchy process to identify factors and criteria influencing cybersecurity and then selected the top 70% of influencing criteria to serve as a reference for railway cybersecurity project management. A total of 25 valid expert questionnaires were collected for weight vector analysis, revealing that the influencing criteria in the top 70% were inability to monitor train occupancy in track sections (locations); inability of controllers to issue commands to safety control systems; inability to provide drivers with information on upcoming signals, block status, and train occupancy; failure to automatically apply brakes when the train exceeds the speed limit; increased risk of catastrophic accidents due to power system security vulnerabilities; and inability of the dispatching system to automatically track train numbers.

With the advent of big data and cloud computing, enterprises, organizations and individuals are increasingly interconnected, leading to a geometric increase in data resource sharing among institutions. However, this trend raises significant concerns regarding user data security and access control, particularly within power systems. This paper proposes leveraging deep neural network models integrated with blockchain technology to process and analyze security information in power big data. We introduce an enhanced approach by combining the Hopfield Neural Network (HNN) with the Simulated Annealing (SA) algorithm, addressing the limitations inherent in the traditional HNN model. Our proposed framework, SA-HNN, is designed to improve the adaptive capabilities of power systems through blockchain-based security strategies. In our experiments, we compared SA-HNN with other machine learning models, including XGBoost, LightGBM and Linear SVC, focusing on storage time and data integrity. The results indicate that SA-HNN outperforms these models in both metrics. Specifically, during a power system security defense test, SA-HNN achieved an algorithm recognition rate exceeding 95%. Furthermore, when evaluating the average transaction time consumption in power blockchain transactions, SA-HNN demonstrated superior performance, handling large volumes of transaction data efficiently with shorter processing times. In terms of user attribute revocation efficiency, SA-HNN exhibited greater file processing capacity and shorter time performance compared with other models. This research highlights the potential of integrating advanced neural networks with blockchain technology to enhance the security and efficiency of power systems. Future work will focus on further refining these models and exploring their applications in broader contexts.

ABSTRACT This research introduces an adaptive energy control strategy for households powered by Sustainable Energy Sources (SES), leveraging Intelligent Grid (IG) technologies. The novelty of this work lies in employing the Intelligent Salp Swarm Algorithm (ISSA), which dynamically adjusts control parameters for real-time optimization, outperforming conventional methods. ISSA efficiently manages appliances, energy sources, and demand prioritization, ensuring optimal energy utilization while addressing SES variability. The proposed model incorporates practical constraints such as customer preferences, energy supply limitations, and ecological unpredictability, ensuring real-world applicability. Three scenarios are analyzed: traditional residences without energy management systems, smart homes with basic SES, and intelligent homes integrating advanced SES. Comparative analysis confirms that ISSA surpasses Genetic Algorithms (GAM) and Particle Swarm Stabilization (PSS) in optimizing energy use. The results highlight ISSA’s ability to enhance system efficiency, reduce costs, and support sustainability goals. This scalable approach effectively addresses modern energy management challenges, contributing to secure, sustainable, and affordable household power systems.

Semi-supervised multi-task learning based framework for power system security assessment

Electric power system security: the case for an integrated cyber-physical risk management framework