The expeditious rise in renewable penetration, inverter-based resources, electric vehicle charging infrastructure, and evolving load composition has driven significant concept drift and new power quality disturbance (PQD) patterns in modern power grids. As a result, contemporary static classifiers become progressively ineffective. To address this challenge, this paper proposes an adaptive lifelong incremental learning framework unified with drift detection, active online learning, and physics-based interpretability for PQD detection and classification under concept drift. Within a unified closed-loop classification architecture, four incremental learning strategies are systematically investigated: Fine-Tuning, Elastic Weight Consolidation (EWC)-Only, Replay-Only, Hybrid (with two additonal configurations of Hybrid-Abrupt and Hybrid-Mixed). The Hybrid strategy combines EWC, herding-based coreset replay, knowledge distillation, prototype regularization, and dark experience replay (DER), together with a dynamically expandable classification head. These methods are evaluated on a testbed of 20 PQDs divided into four tasks. The Hybrid method achieves the maximum accuracy () with a forgetting rate of just and a Macro F1 of , validated across three seeds. A comparative analysis against continual learning baselines—iCaRL, GEM, DER, ER-ACE, and Cumulative-Oracle—demonstrates the competitive performance of the Hybrid approach. For drift detection, a KNN-embedded technique is evaluated against the established ADaptive WINdowing (ADWIN) and Drift Detection Method (DDM) techniques and is found to consistently achieve superior performance under evolving disturbance conditions. The proposed models use a ResNet-MLP-based encoder backbone; to validate its strength, the Hybrid method is additionally examined using Plain-MLP, Deep-MLP, MLP-No-BN, Transformer-Encoder, CNN-1D, and CNN-LSTM encoders. Physics-aligned features—root-mean-square (RMS) voltage and total harmonic distortion (THD)—serve dual roles as interpretable monitoring metrics and drift attribution indicators. A detailed computational complexity analysis is conducted, covering time, space, computational (FLOPs), and sample/scalability complexity. The six frameworks are further assessed under 27 noise scenarios spanning 9 categories—Pink and Brownian noise, additive white Gaussian noise (AWGN), impulsive noise, load harmonics, non-stationary mixed noise, communication signal drop, environmental flicker, and measurement bias. For the first time in the PQD literature, a comprehensive bifurcation analysis identifies the regime boundaries governed by EWC weight and noise intensity , complemented by a parametric sensitivity analysis of the proposed models. All six models are further exposed to (i) grid topology and (ii) renewable and power-electronics disturbance scenarios for realistic, system-level examination. Finally, validation on the XPQRS benchmark dataset (four tasks) shows the Hybrid-Mixed model achieving the best accuracy of 85.75 % with a forgetting rate of just 1.50 %. The complete framework was further validated through Processor-in-the-Loop (PIL) testing on a Raspberry Pi 5, confirming functional correctness of the full inference pipeline under embedded execution with an inference latency of approximately 0.15 ms/sample and a memory footprint of only 0.40 MB. Altogether, the developed models offer an effective, interpretable, and computationally-efficient approach for PQD detection under evolving grid conditions, providing a standard of comparison for future approaches in PQD analysis.

The integration of new generation sources and loads has transformed the power grid into a more flexible and interactive system, though it has also disturbed power quality. Consequently, accurate detection and classification of Power Quality Disturbances (PQDs) are essential for implementing effective corrective actions in smart grids. However, existing methods often struggle to simultaneously capture complex spatio-temporal dependencies and maintain computational efficiency, particularly for combined disturbances where multiple fault patterns overlap. To address this gap, this paper proposes a novel hybrid deep learning framework. The novelty lies in the synergistic integration of a powerful pre-trained Convolutional Neural Network (EfficientNet-B7) for efficient spatial feature extraction, a Bidirectional Long Short-Term Memory (Bi-LSTM) for deep temporal modeling, and a Cross (X)-attention mechanism to adaptively focus on the most discriminative features. A rigorous evaluation confirms the effectiveness of this approach. The model achieves an overall accuracy of 99.62%, precision of 99.7%, recall of 99.51%, and an F1-Score of 99.51% on a clean, balanced dataset. The model's robustness is further demonstrated by its high accuracy on unbalanced data (99.01%) and under challenging 20 dB noise conditions (97.95%). With a rapid average inference time of less than 5 ms, these findings establish that our proposed framework provides a compelling combination of accuracy, robustness, and efficiency, making it a highly promising solution for real-time monitoring in modern smart grid systems.

The integration of Information and Communication Technologies (ICT) in power system applications, such as Load Frequency Control (LFC), increases the vulnerability to cyber-attacks. Among these, False Data Injection Attacks (FDIA) targeting sensor-controller or controller-actuator signals can destabilize systems with varying loads and renewable energy sources. This paper proposes a Fractional Order Sliding Mode Controller (FOSMC) to ensure frequency stability and resilience in an islanded system under controller-actuator FDIA and external disturbances. An observer is incorporated to estimate system states and detect FDIA signals, with its parameters obtained via Linear Matrix Inequality (LMI) techniques. A fractional order sliding surface is designed to enhance robustness, and system stability is analyzed using the Lyapunov method. The proposed controller is validated through a Model-in-the-Loop (MiL) OPAL-RT setup. Experimental results confirm that the resilient controller maintains stability under different FDIA scenarios and outperforms existing methods in dynamic response.

Power quality disturbances (PQDs) are significant irregularities in electrical power systems that can negatively impact system stability and delicate equipment. To ensure the re liability of the power system, PQDs must be classified accurately and effectively. This work investigates the application of Vision Transformer (ViT) based architectures for classifying PQDs, which are preprocessed into spectral images. The performance of the proposed model is evaluated and compared against the convolutional neural networks (CNNs). The three vision transformer variants considered in this work include the Basic ViT model, the Swin Transformer model, and the Data-Efficient Image Transformer (DeiT). Among these three variants, DeiT employs a knowledge distillation strategy, achieving accuracies of 99.33% and 99.38%, and offers qualitative interpretability through attention maps. Through comparative analysis with CNN architectures, such as EfficientNet and DenseNet, this paper highlights the potential of ViT-based models for PQD classification and proposes future extensions toward real-world validation and improved teacher-student frameworks.

In a wind power plant (WPP), cyberattacks are possible due to existing vulnerabilities in communication protocols, multi-level control loops, extensive data transfers, and the remote locations of wind turbines (WTs). The physical impacts of such threats against WPP can propagate into the wide-area grid and result in a blackout. This paper introduces a novel attack model in which adversaries exploit cybersecurity vulnerabilities in wind power plants (WPPs) to conduct false data injection (FDI) attacks. By using sinusoidal signals, these attacks can destabilize poorly damped modes (PDMs) within bulk power grids, posing a risk of widespread blackouts even with low WPP integration. We propose a model-based detection method that analyzes spectral data from WPP control centers, in compliance with IEC 61400-25. A detection threshold is established using power spectrum density (PSD) distributions, and a fuzzy logic controller (FLC) is incorporated to manage operational uncertainties. Validation on the New England 39-bus and Simplified 16-Generator Australian Power System confirms the effectiveness of the proposed detection approach in the presence of either Gaussian or non-Gaussian noises and coordinated attacks.

This paper presents a novel methodology that utilises distributed energy resources (DER) to serve as providers of frequency control ancillary services (FCAS) within the framework of the Australian National Electricity Market (NEM) system. Addressing pertinent industrial challenges and research gaps regarding the consideration of DERs for FCAS provision, the paper introduces a novel coordination technique designed to facilitate harmonious interaction among various DER types and between DERs and non-DERs FCAS sources. Furthermore, it tackles the issue of services regionalisation within the NEM's FCAS framework by proposing a strategy for optimally allocating FCAS across different power regions within the NEM. The methodology is developed based on a mathematical model that simulates the NEM and adheres to the National Electricity Rules (NERs) defined for the NEM. The numerical results confirm that the proposed method effectively maintains system dynamic performance within acceptable grid code standards, while utilising DERs as the primary source to support FCAS.

This paper addresses frequency regulation under operational constraints in interconnected power systems with high penetration of inverter-based renewable generation. A two-layer control architecture is proposed that combines optimized droop and Virtual Synchronous Machine (VSM) primary control with a Model Predictive Control (MPC) secondary layer operating at realistic control-room update rates. Unlike recently proposed approaches, the proposed framework integrates MPC within existing grid control structures, enabling constraint-aware coordination. A reduced-order frequency response model is systematically derived from a detailed grid model using Hankel singular values, and a reduced-order Kalman-Bucy observer enables state and disturbance estimation using only measurable outputs. Evaluation using representative data from the Kingdom of Saudi Arabia demonstrates effective frequency regulation under realistic operating conditions.

This industry-oriented paper proposes a novel sophisticated frequency control system for the Australian National Electricity Market (NEM) power system considering the special design of its current automatic generation control system, which does not involve interconnectors between different power regions, resulting in technical challenges for both stability and security of the system. The proposed new method converts the NEM centralised frequency control system to a fully decentralised one with virtually consideration of interconnectors' power, thus the stability and security would be improved and the high fluctuations in interchanged power smoothed without extra costs of infrastructure and procurement of frequency control ancillary reserves (FCASs). The novel idea is to model the interconnectors' power deviation as unknown input to operator in each power regions, like load disturbances and renewable power variations, using dynamic observation techniques, for enabling the proposed method capabilities. The method is theoretically verified that it can enhance stability and security and stabilise both local frequencies and power exchanges. The experiment results confirm the capability of the proposed method for the NEM system and its superiority over the existing adopted approaches in industries.

The rapid expansion of renewable energy sources (RES) in modern power systems has increased the occurrence of power quality disturbances (PQDs), affecting system performance. This paper introduces a novel approach combining Fourier Synchrosqueezing Transform (FSST) for time-frequency representation (TFR) with Vision Transformer (ViT) for classifying PQDs. By incorporating global characteristics, the ViT model increases classification accuracy, while GradCAM visualization improves interpretability. Experiments on synthetic datasets with single-type signal disturbances validate the proposed approach, showcasing superior performance in metrics compared to traditional methods. With a 99.73% accuracy rate, the proposed approach shows promise for precise classification and efficient power quality event monitoring.

Recent Italian regulation rewards Local Energy Communities (LECs) through two distinct channels: an incentive for virtual shared energy and market access for distributed batteries that provide up-regulation. These incentives often conflict, as charging batteries to maximize the shared energy limits the capacity to provide ancillary services, and vice versa. Currently, quantitative tools for effectively balancing these objectives are lacking respecting the electrical constraints of the low-voltage grid. To fill this gap, a multi-objective optimization is proposed that co-maximizes the revenue from up-regulation and the virtual shared energy reward, under the constraint that the daily energy bill does not exceed a predefined baseline. The implemented mathematical programming formulation utilizes multi-objective second-order cone programming (SOCP) with linear constraints to incorporate the network's physical constraints. Linearization and decomposition techniques are employed to simplify the problem. By adjusting the physical constraints of the network, the impact of energy communities on the distribution network can also be evaluated with different objectives. The model allows the representation of real peer-to-peer trading, quantifying its effects on both revenue streams and voltage profiles as well as power losses. Trade-off analyses performed on an 84-bus radial distribution network, under both constant and variable prices, show that the framework adapts smoothly to market volatility, highlighting when it is advantageous to prioritize up-regulation and when it becomes preferable to maximize the virtual shared energy incentive.

Load frequency control (LFC) is a crucial application in modern power systems as it ensures the system frequency remains within an acceptable range through demand control and active power generation. However, as information and communication technology (ICT) becomes more prevalent in power system, there are both opportunities for improved reliability and efficiency as well as potential security threats. This article proposes an LFC approach that takes into account the simultaneous occurrence of false data injection (FDI) attacks on the sensor-controller side, disturbances in the states of the system and time delays in the controller-actuator side. To tackle these challenges, a slide mode observer is utilized to estimate system states and detect cyber-attack signals as an extra virtual state. Subsequently, a cyber-attack-resilient predictor slide mode controller is designed to establish a robust control law capable of overcoming system challenges, when all system states are not within reach. Therefore, the robust control law is designed based on the estimated states, cyber signal and measured system output. By integrating these techniques, the proposed methodology offers a promising solution to enhance the resilience and performance of power system faced with cybersecurity threats. It improves the response speed of the system and minimizes the maximum overshoot compared to some published resilience control laws, thereby ensuring secure and reliable load frequency control. Additionally, reducing the number of sensors in the system helps to reduce overall costs. Finally, the performance of the controller is also verified in real-time using the OPAL-RT simulator testbed.

The electricity generated from the present-day large capacity doubly fed induction generator (DFIG) installed wind farm is generally transmitted to utility grid via medium or high voltage transmission line (TL). Due to the restriction of building new TLs, series compensated TLs are some cases preferred for such applications. But, the nonlinear output power versus wind speed relation, control strategies of power electronic interfaced DFIG-wind turbine generators and the nonlinear operation of the thyristor-controlled series capacitor (TCSC) during fault impose adverse impact on the performance of the conventionally used distance relaying-based TL protection schemes. In this article, an improved fault detection and classification technique is proposed to assist distance relay in ensuring fast and reliable protection to TCSC compensated TL linked to DFIG-installed wind farm. In this method, a feature called transient monitoring indexed (TMI) is derived from the measured three-phase currents at the relay location for fault detection and TMI-assisted support vector machine is employed further for fault classification. Performance of the proposed scheme is validated on various fault and nonfault transients simulated on a test power system through MATLAB/Simulink. This protective scheme is farther validated throughout real-time assembled dSPACE DS 1104 control prototype hardware. The superiority of the proposed method is also demonstrated through comparative assessment results with few existing techniques. The overall results justify the merits of the proposed method for fast and accurate detection and classification of faults in such crucial TLs.

Cyberattacks pose a significant threat to modern power systems due to the interaction of their physical components with information and communication technologies. A critical power system application that is directly affected by such malicious activities is the Load Frequency Control (LFC) system. The goal of the LFC is to maintain the power balance of the grid by sensing frequency deviations and regulating the output of the generators. In this paper, an innovative False Data Injection Attack (FDIA) estimation method is proposed for LFC along with an efficient cyberattack-resilient control design. The presented attack mitigation technique employs novel sliding mode techniques combined with an unknown input observer to estimate the launched FDIAs. Then, the estimated attack vector is used in the control loop to eliminate the cyberattack impact on LFC. The introduced method can tackle FDIAs that target both measurements and control signals and is resilient against external system disturbances. For the experiments, several real-world features of power systems are considered, such as nonlinearities, network delays, diverse types of tie-lines and multiple topologies of interconnected power regions, along with Hardware-in-the-Loop simulations for real-time assessment. The results verify the effectiveness and the feasibility of the proposed method, its scalability over various power systems and its superiority in comparison with other techniques.

The integration of modern power systems with information and communication technologies exposes them to various cyber threats. Load frequency control (LFC) is a communication-based automation in power systems that regulates the frequency of the grid. Its critical role makes it a highly attractive target for adversaries. This paper proposes a novel detection and isolation method of False Data Injection Attacks (FDIAs) against LFC. The defense method employs sliding mode observation techniques to detect FDIAs against LFC in real-time and discover which parts of the control loop have been compromised. Attacks are identified by comparing the generated residuals with a specific threshold that is designed in an adaptive manner. The proposed method is able to successfully distinguish the FDIAs from other system disturbances and is robust against uncertainties in power system parameters and noisy measurements. The effectiveness and scalability of the proposed defense method are confirmed on realistic power system models, considering nonlinearities, different topologies and diverse types of transmission links.

AbstractThis research proposes the application of fractional‐order sliding mode control (FOSMC) at the primary controller level to improve the stability of an islanded microgrid by adjusting its voltage and frequency. The control strategies used in the microgrid are performed in two levels (primary and secondary) in the islanded mode. Practically, most previous studies have worked to improve the primary controller. Droop control is one of the most commonly used methods at the primary level and is adopted in this study as well. The sliding mode control (SMC) strategy is normally used to control linear equations. Thus, the non‐linear microgrid equations were transformed into some linear ones using the input‐output feedback linearization technique. Further, a fractional sliding surface was acquainted. The sliding surface and FOSMC were designed to reject system uncertainties and organize the voltage and frequency. Design parameters were chosen using the Lyapunov stability theorem. The validation of the proposed method using Simulink‐MATLAB confirms its effectiveness in enhancing level power sharing, regulating frequency, and maintaining voltage stability across the system.

Contemporary power systems are threatened by cyberattacks as a result of their interactions with information and communication technologies (ICT). Load frequency control (LFC) system is a fundamental power system application whose role is to maintain the power balance of the grid by sensing frequency deviations and properly regulating the output of the generators. Due to its dependence on ICT, the LFC is directly exposed to digital threats. This paper introduces a novel method for the estimation and mitigation of False Data Injection Attacks (FDIAs) to address cyber threats against LFC. The proposed method utilizes an innovative sliding mode technique to approximate the launched FDIAs against the LFC. Then, the estimated attack vector is added to the control loop to eliminate the cyberattack impact on LFC, forming an attack-resilient control strategy. This approach is robust against external system disturbances as it is designed to be completely decoupled from them. For experimental validation, several real-world power system features are implemented, including nonlinearities, network delays, diverse types of tie-lines and multiple topologies, and a Hardware-in-the-Loop testbed is developed for real-time testing. The results confirm the effectiveness, the feasibility and the scalability of the proposed defense method along with its superiority compared to other similar techniques.

In the recent years there has been a significant increase in the interest surrounding the interconnection of AC and DC networks due to its potential to improve the power systems reliability. Despite the potential advantages, however, the AC and DC grid coexistence can pose challenges if not appropriately managed. In this scenario, this paper presents an enhanced control scheme enabling the use of Virtual Synchronous Generators (VSGs) as elements of interconnection between AC and DC networks. The proposed solution enables VSG operation with both fast dynamics for DC voltage regulation and slow dynamics for AC inertia emulation. This approach accommodates the contrasting requirements arising from the distinct mechanisms of DC voltage regulation and AC frequency regulation. Simulation and experimental results obtained with a 8 kVA three-phase converter prototype are provided to validate the effectiveness of the proposed technique and prove its better performances with respect to the solutions currently presented in literature.

The increasing integration of renewable energies, while beneficial for environmental and economic sustainability through decarbonization, poses challenges to frequency stability due to the intermittent nature of renewable power supply. To facilitate smoother integration into the main grid, this study proposes a resilient distributed load frequency control (RDLFC) strategy with a hierarchical structure. At the lower level, wind energy integration is managed using a model predictive control framework enhanced by an improved event-triggered scheme, which can effectively trigger key feedback signals at critical points and tolerates imperfect event modeling and generator dysfunctions. Plug-in electric vehicles are also utilized for fast frequency regulation. At the higher level, the linearized model is improved with an uncertain parameter matrix to account for variations in steady-state operating points due to renewable integration. A robust performance index is incorporated to derive stability conditions, even in the presence of temporary faults in phasor measurement units (PMUs). Validation results confirm the effectiveness of the proposed RDLFC strategy in handling temporary PMU faults.

This industry-oriented paper presents an overview and in-depth analysis of previous and current situations of power system frequency response and control in Australia. The evaluation of different services provided by different electricity market players under the supervision of the Australian Energy Market Operator (AEMO) as an independent system operator provides lessons and an understanding of the current operation status with its real challenges and opportunities from frequency stability and security perspectives. Based on the evaluation of the current situation and future national planning, a number of research gaps and industrial technical issues are identified, and some perspectives on future research directions are presented to help move the power system transformation toward almost 100% renewable and more secure energy systems.

This paper investigates a particle filter (PF)-based fully-decentralised dynamic state estimation (DSE) method for interconnected multi-machine power systems. The PF-based observer is developed to dynamically estimate the states of the 7th-order dynamic model of synchronous machines that are either inaccessible and/or highly noisy. It is assumed that the proposed PF-based robust decentralized observer for a particular synchronous generating unit relies on typical output measurements available from phasor measurement units (PMUs) installed at its terminal. The performance of the presented observer is investigated using the benchmark model of the IEEE 68- bus system considering a detailed sub-transient representative model of synchronous machines with different excitation and control systems. The presented estimation framework works successfully and accurately under various transient events, such as load perturbation, faults, and changes in network topology, while accounting for different errors and sampling rates in measurements. The accuracy and robustness of the presented dynamic estimator in the case of Gaussian and non-Gaussian noisy measurements are verified. The paper also develops an approach-based PF to detect bad data and introduces a new metric based on the computation Cramér–Rao Low bound (CRLB) for evaluating the dynamic estimation performance. The introduced PF-based DSE improves the system resiliency by providing the system operator with the monitoring and observation capability of the system in a real-time manner to perform the proper corrective and protective actions in case of any events. The comparative study with other sophisticated dynamic state estimators confirms the brilliance, robustness, and superiority of the presented PF-based dynamic state estimation for multi-machine systems, and its practical and implementation feasibility.