Veerapandiyan Veerasamy

The increasing penetration of renewable energy sources (RES) and plug-in hybrid electric vehicles (PHEVs) has introduced significant frequency instability in interconnected microgrid (MG) systems, necessitating adaptive and robust control strategies for reliable operation. This paper proposes a fuzzy-explainable neural network (FxNN)-based distributed fractional-order PID (FOPID) controller for active frequency regulation in interconnected microgrids. The control problem is formulated within a Lyapunov-based optimization framework, where system stability is ensured through a recursively updated energy function and differential learning dynamics of the explainable neural network. The proposed cascaded FxNN-FOPID controller is evaluated under varying load disturbances and intermittent renewable power. Simulation results demonstrate superior dynamic performance compared with conventional single-loop FxNN and PSO-GSA-based controllers. The proposed controller achieves a minimum settling time of 3.0 s, representing improvements of 14.3% and 30.2%, respectively. Furthermore, the integral absolute error (IAE) is reduced to 0.6292 × 10 ⁻³ , 0.5929 × 10 ⁻³ , and 0.6000 × 10 ⁻³ for MG1, MG2, and MG3, respectively, while the integral time absolute error (ITAE) is reduced by up to 93.6%. The controller also minimizes frequency oscillations with a peak magnitude of 0.0003 p.u. and achieves improved Integral of Squared Error (ISE) 0.1040 × 10 ⁻⁵ and Integral of Time-weighted Squared Error (ITSE) 0.9918 × 10 ⁻³ values. Owing to its adaptive gain tuning capability, the proposed controller maintains robust performance without requiring manual retuning under varying operating conditions. Comparative analysis confirms that the proposed cascaded FxNN-based distributed FOPID controller provides faster dynamic response, improved disturbance rejection, and superior low-frequency oscillation damping, making it an effective solution for reliable frequency regulation in renewable-energy-integrated microgrid systems. Furthermore, the proposed framework supports Sustainable Development Goals (SDG 7: Affordable and Clean Energy, SDG 9: Industry, Innovation and Infrastructure, and SDG 13: Climate Action) by facilitating resilient microgrid operation, enhancing renewable energy integration, and promoting low-carbon power systems.

High impedance faults (HIFs) present significant challenges in power systems, particularly when an electrical wire contacts a high-resistance material, leading to low currents that are difficult for traditional relays to detect. With the increasing integration of photovoltaic (PV) systems, these challenges are exacerbated due to the complex behavior of PV-generated signals. This study aims to enhance the detection of HIFs in PV-integrated systems using advanced machine learning techniques. The approach employs various classifiers, including artificial neural networks, support vector machines (SVMs), decision trees, and random forest (RF) to improve fault identification accuracy. A MATLAB/SIMULINK simulation was conducted on an IEEE 13-bus system with a 300-kW solar PV plant. The discrete wavelet transform (DWT) with the db4 wavelet was used for feature extraction, focusing on phase energy values. The classifiers were evaluated under different scenarios, such as normal operation, load switching (LS), capacitor switching (CS), HIF, and line-to-ground (LG) faults. The RF classifier outperformed others, achieving a fault detection accuracy of 99.4083%, demonstrating its robustness in adapting to various fault conditions. The Naive Bayes (NB), multilayer perceptron (MLP), and logistic regression (LR) classifiers achieved lower accuracies of 78.6982%, 76.9231%, and 80.4734%, respectively. These results indicate a significant improvement in fault detection capability, enhancing the stability, reliability, and resilience of electrical grids integrated with PV systems. The findings suggest that the RF classifier is highly effective for HIF detection, which is crucial for the protection and efficient operation of modern power grids with high renewable energy penetration.

Although distributed renewable energy sources (DRESs) provide a sustainable solution to future microgrids (MGs), their fluctuant power outputs can incur frequency instability. The work studies the load frequency control (LFC) for MGs with the integration of wind energy under a hierarchical architecture. At the DRES level, a model predictive control method is employed together with an intensified event-triggered scheme considering multiple historic released signals to improve the computation efficiency. At the MG level, robustness specification is addressed in mean-square asymptotic stability to relieve the fluctuations caused by wind power penetration. Furthermore, the phasor measurement unit (PMU) failure and intermittent transmissions are considered in the control design, leading to the resilient control policy. Besides, this article extends the applicability of conventional small-signal LFC model by adding an uncertain matrix to tolerant the parameter variation due to the shift of the steady-state operating point caused by wind energy integration. The closed-loop performance based on the deployed resilient LFC strategy is verified through hardware-in-the-loop experiments, by which the frequency regulations against PMU failures and intermittent communication at different levels are effectively exhibited.

Rotor angle stability, which involves electromechanical oscillation damping and control, is very important in maintaining the stability of modern power grid systems. Renewable energy sources like wind energy are undergoing massive integration into modern power grid systems to meet energy demands and decarbonize power grid systems of carbon emissions from fossil fuel generators. To enable increased integration of wind renewable energy sources, precise models are needed for research and analytical purposes. Wind renewable energy is generated through a wind energy conversion system (WECS); one such conversion system is the doubly fed induction generator (DFIG) system. In this study, a precise model of a DFIG-WECS was modeled and integrated into the IEEE’s two-area Kundur power test system, which represents the available power grid system, and is also a multimachine power system using the Matlab/Simulink 2023 software. A damping controller known as the power system stabilizer (PSS), whose optimal parameters were obtained using artificial eco-system optimization (AEO), was also incorporated into the integrated power grid system to control and damp electromechanical oscillations. The results showed that the PSS damping controller effectively damped electromechanical oscillations in the integrated power grid system.

The integration of renewable energy sources in modern microgrid power systems has a significant impact on frequency stability due to reducing inertia and damping coefficient. This article employs a virtual inertia control (VIC) based on frequency deviation derivatives to emulate the system inertia and damping coefficient characteristics of traditional synchronous generators. Coordination between the global controller (load frequency control) and the VIC is implemented. The parameters of both the secondary and virtual control are tuned using a novel hybrid sparrow search algorithm with mountain gazelle optimizer algorithm. The simulation results demonstrate a substantial improvement in mitigating the low inertia of the power system when exposed to consecutive rapid load changes, utilizing the suggested algorithm on comparing with the hybrid sparrow search algorithm based on grey wolf optimizer.

This paper introduces a robust approach, integrating a Virtual Inertia Controller (VIC) with a modified demand response controller for an islanded Multi-Microgrid (MMG) system, accommodating high levels of Renewable Energy Sources (RESs). In these MGs, the low inertia in the system has an undesirable impact on the stability of MG frequency. As a result, it leads to a weakening of the MGs overall performance. A novel fractional derivative virtual inertia is integrated into the VIC loop to address this issue. This enhancement aims to fortify the MG’s stability and robust performance, particularly when facing contingencies. Furthermore, a modified demand response controller has been incorporated into the proposed inertia control technique to mitigate the frequency fluctuations and reduce stress on the energy storage system (ESS). Fractional Order Proportional Integral Derivative (FOPID) controllers have been employed to regulate the active power output of the biodiesel generators and the Geothermal station in the MG. The hybrid sparrow search and mountain gazelle optimizer algorithm (SSAMGO) optimizes the parameters for the three-loop controller. Time-domain simulations assess the effectiveness of proposed controllers in enhancing system frequency stability. SSAMGO’s performance was comprehensively evaluated, comparing it to various optimization algorithms in diverse scenarios. The results obtained from the MMG system demonstrate that utilizing the proposed controller technique, optimized with hybrid SSAMGO parameters, yields notable improvements in settling time by 24.68%, 46.20%, 7.52%, and 61.01%, steady-state error values by 72.56%, 98.18%, 98.73%, and 6.67%, undershoot by 105.76%, 144.23%, 19.23%, and 7.69% compared to other state-of-the-art algorithms presented in the literature. Finally, the proposed control technique’s effectiveness and robustness are assessed in comparison to conventional inertia control across various system scenarios. These scenarios encompass random load demand fluctuations, real-time changes in RES, and a wide spectrum of system operations, including situations with reduced damping and inertia and high levels of load variation.

Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty, a generic projection and decomposition (P&D) approach is proposed in this letter. The canonical min-max-min two-stage robust optimization (TSRO) model with coupling constraints is equivalent to a concise robust optimization (RO) model in the version of mixed-integer linear programming (MILP) via feasible region projection. The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm, which ensures the fast convergence to a high-quality solution in the distributed optimization. Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

This article proposes the federated-learning-based second-order recurrent neural network (SORNN–FL) for distributed frequency control of a multimicrogrid (MMG) system. The problem is conceptualized as proportional–integral derivative consensus control using the Lyapunov function, which depicts the behavior of recurrent neural network (RNN). The parameters of the controller are obtained by deriving the dynamics of energy function to update the state of neurons. During the learning process, FL of weights is adopted to improve the performance of the network by increasing the communication among the neurons. The effectiveness of the developed SORNN–FL controller is tested for the MMG system, and its performance is compared with those of the MMG system using SORNN, RNN, and the conventional method of tuning. The results obtained reveal that the proposed higher order control gives an improved performance during uncertainties and communication failure. Specifically, we use a differential privacy mechanism to secure the data from false data injection attacks by the invaders. The various extensive cases studied on the MMG system demonstrate that the proposed privacy-preserving-based SORNN–FL scheme is robust and efficient. Even if a communication failure and a malicious attack occur in the MMG system, our proposed control scheme can regulate the system frequency and maintain system stability. The real-time validation has been demonstrated at the 0.4-kV microgrid test-bed system in the laboratory.

This paper presents a blockchain-based decentralized frequency control of an islanded microgrid (MG) using a novel federated learning fractional order recurrent neural network (FL-FORNN). A self-adaptive proportional-integral-derivative (PID) controller is proposed using FL-FORNN to handle the uncertainties of power generation by the prosumers while participating in the trading of the islanded MG. In this work, the frequency oscillations during peer-to-peer (P2P) energy trading are regulated through the appropriate design of an adaptive controller and the implementation of a smart contract participation matrix (SCPM) for balancing the generation and consumer demand. A Proof-of-Authority (PoA) private blockchain-based SCPM has been developed to secure the contract among peers. The SCPM provides the power demand information from a consumer to a prosumer who participates in the P2P energy market. It also provides power reference to the prosumer distributed generation as a supplementary control, in addition to the secondary frequency control of the system. To study this, an islanded MG comprising four source nodes (prosumers) and three demand nodes (consumers) are tested with the implementation of the PoA-based blockchain framework. With its SCPM calculation running in the blockchain framework, the decentralized control of prosumers is implemented by interfacing the OPAL-RT with Raspberry Pi devices. The robustness of the proposed FL-FORNN controller has been verified with the FORNN tuned PID controller. Furthermore, a comparative analysis is made with the centralized scheme and traditional decentralized frequency control technique.