Muhammad Sharjeel Javaid

The opaqueness of vendor-specific black-box models of inverter-based resources (IBRs) is a barrier to the systematic analysis of IBR-induced sub-synchronous oscillations (SSO). Existing approaches via impedance scan require isolating each IBR from the power system and interfacing it with a voltage or current source. However, this procedure is not feasible when the IBR device is embedded within the rest of the system (RoS). In this paper, we propose an augmented eigensystem realization algorithm (ERA)-based method to estimate the IBR model in situ, i.e., while the IBR remains connected to RoS. The core idea is to disentangle the IBR's impedance spectra from its closed-loop voltage and current responses to a pulse probing. Notably, the augmented ERA enables automated model order selection, ensuring scalability for realistic IBR-rich zones of a power grid. The estimated linearized IBR models are then combined with the known RoS model, which yields a linearized state-space model of the overall power system. For validation, the case studies demonstrate the applicability and scalability of the proposed method. The frequency-domain and time-domain responses of the estimated IBR models closely match those of the actual models. Modal analysis accurately identifies the IBR with dominant contribution to poorly damped SSO, therefore enabling targeted and effective mitigation.

Unmanned Aerial Vehicle Path Planning (UAVPP) in obstacle-rich environments requires trajectories that are collision-free, threat-aware, and feasible under practical flight constraints. This study proposes an Intelligent Cooperative Differential Evolution approach, reffered to as MuCDEA, to improve the adaptability and robustness of conventional Differential Evolution (DE) for UAVPP. MuCDEA integrates complementary mechanisms from JADE, CoDE, EPSDE, SaDE, MIDE, and SHADE through adaptive strategy selection and cooperative evolution. The optimization model combines path-length (fuel) cost and threat exposure with explicit pitch and yaw constraints that enforce actuator-feasible maneuvering bounds. The proposed framework is evaluated on 20 benchmark UAVPP cases covering 2D and 3D scenarios with varying obstacle distributions and pathh discretization levels, and it is compared against 11 state-of-the-art DE variants and several widely used optimization methods using the CEC-2022 ranking methodology. Results show that the cooperative configuration MuCDEA24 achieves the best overall ranking and consistently produces feasible trajectories across the tested cases, indicating that cooperative DE strategies provide an effective and controller-compatible solution for constrained UAVPP.

Optimal sizing of hybrid renewable energy systems for desert camps is a multi-objective problem that must account for cost, reliability, component degradation, and uncertainty. This paper introduces an improved multi-objective neural architecture search (IMONAS) framework for hybrid nanogrid sizing in the desert environment of Hafr Al-Batin, Saudi Arabia. The framework combines neural optimization, stochastic uncertainty modeling, and explicit battery degradation modeling, a combination not addressed in the reviewed studies for this application. Six test cases are examined by varying uncertainty assumptions, battery degradation, and the annual duration of uncertain operation. For each case, IMONAS provides Pareto-front solutions that specify the photovoltaic, diesel generator, battery autonomy, and inverter choices while minimizing the cost of energy (COE) and the loss of power supply probability (LPSP). IMONAS is compared with the original MONAS and five other multi-objective optimization methods. In addition to visual Pareto-front comparisons, the assessment uses Pareto-dominance indicators, namely the C-metric and an aggregated score derived from pairwise C-metric comparisons across the algorithms and cases. The results provide a validated sizing framework for remote arid-region nanogrids under uncertainty and battery degradation.

Recent advancements in computational hardware and parallel processing are driving developments in metaheuristic algorithms. These advancements help solve increasingly complex real-world optimization problems. As a result, more sophisticated and computationally demanding algorithms can now be implemented. This paper presents a comparative performance evaluation of ten modern metaheuristic algorithms for the optimal design of Electromagnetic Devices (EMDs). It also evaluates seven well-established metaheuristics, which are referred to as reference algorithms. In addition to the comparison, a novel hybrid optimization strategy called Multiple Combined Algorithms for Optimization (MuCAO) is proposed. MuCAO probabilistically combines the best-performing algorithms to leverage their complementary strengths. All algorithms, including MuCAO, were tested on six benchmark problems with various complexities and design variables. These benchmarks include analytical models and problems based on the Finite Element Method (FEM). For validation, the approach was also applied to a real-life application, which is Sidelobe Level Reduction in a Circular Antenna Array (CAA). The results show that MuCAO outperformed all other algorithms and achieved the highest overall ranking. Three modern metaheuristics followed. The best-performing reference algorithm ranked lower, with DE in fifth place. The study confirms that modern metaheuristics generally offer superior performance for EMD design and optimization compared to traditional metaheuristics.

Initiatives to decarbonize contemporary power systems will lead to power systems dominated by inverter-based resources (IBR). These IBRs are broadly classified into grid-following (GFL) and grid-forming (GFM) inverter control regimes. In this paper, a small-signal state-space model is formulated for a 100% IBR-dominated power system. This formulation is then used to perform stability analysis on the IEEE 9-bus system. The analysis thoroughly investigates the impacts of varying GFM inverter penetration on system stability. The paper also discusses the system behavior in the wake of varying shares of different GFL inverter control schemes. The cause of instability is ascertained by investigating the participation factor of each component. Moreover, the paper examines the effects of phase-locked loop (PLL) and power synchronization loop (PSL) parameters on determining the minimum GFM penetration necessary for ensuring stable system operation. It has been found that the inverter type with the highest capacity is not always the biggest contributor to instability. Also, increasing the proportional coefficient and decreasing the integral coefficient of the PLL of GFL inverters allow the system to be operated under lower GFM penetration. Similarly, increasing the moment of inertia or damping coefficient of the GFM inverter’s power synchronization loop has a similar effect. The conclusions and the underlying theoretical analysis are verified by time-domain simulations of electromagnetic transient (EMT) models in Matlab/Simulink.

Low-frequency electromechanical oscillations ($< $ 2Hz) are time-separated from faster network dynamics, allowing network dynamics to be safely neglected in positive-sequence RMS (RMS$+$) studies. However, with increasing shares of inverter-based resources (IBRs), sub-synchronous oscillations (SSOs) occur at higher frequencies ($>$ 5Hz) within the electromagnetic timescales. The shift challenges using RMS$+$ tools for planning IBR-dominated grids, as the time-scale separation is no longer as distinct as in synchronous machine-based systems. This paper demonstrates that relying on RMS$+$ studies in high-IBR scenarios can lead to erroneous conclusions about SSO, including a false assurance of stability. We explain how the interaction between IBR control and network dynamics affect the damping of SSO. This highlights the need for EMT-$dq$ with network dynamics in a synchronously rotating reference frame as a middle ground between EMT-$abc$ (point-on-wave) and RMS$+$ for studying IBR-driven SSO. EMT-$dq$ is computationally simpler than EMT-$abc$ and allows frequency-domain analysis for deeper insight and effective SSO mitigation. This emphasizes the necessity of established (rather than bespoke) EMT-$dq$ tools to quickly screen SSO-prone scenarios for detailed investigation in EMT-$abc$.

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints.

Brain Computing Interface (BCI) technology commonly requires AI model for classifying electroencephalogram (EEG) signals, however, EEG data is highly subjective to users thus creating a generalized AI model for EEG classification is an active area of research. Model-Agnostic Meta-Learning (MAML) has gained significant attention in the machine learning community due to its ability to quickly adapt to new tasks using a small amount of data. In this conference paper, we explore the potential of using MAML AI model such that it requires few data samples to yield accurate results for new users’ EEG data. By applying a multitask learning approach, treating each user as a new task, we deduce that MAML may be a useful tool for improving the adaptability and generalization of BCI systems.