Journal of Fuzzy Systems and Control

Optimization of Photovoltaic (PV) Hosting Capacity in 20 kV Distribution System Using Grey Wolf Optimizer (GWO) Algorithm

2026-07-22T03:56:58+00:00

The transition toward sustainable energy systems to mitigate global warming caused by greenhouse gas emissions from fossil fuel-based power generation has accelerated the integration of photovoltaic (PV) systems into distribution networks. However, massive and uncontrolled PV integration may lead to operational issues in power systems. Therefore, hosting capacity studies are required to determine the maximum PV capacity that can be integrated without violating technical operating constraints. Due to the complex, non-linear, and non-convex nature of the hosting capacity problem, effective optimization techniques are necessary. This study proposes the Grey Wolf Optimizer (GWO) algorithm to determine the optimal location and capacity of PV with the objectives of maximizing the PV penetration while minimizing system power losses. The Site Planning Model (SPM) method is employed to identify candidate buses for PV installation, thereby reducing space and computational time. By coupling GWO's global search with SPM-based candidate-bus pre-selection, this study reduces the optimization search space while preserving solution quality. The IEEE 33-bus 20 kV test system is used to evaluate the performance of the proposed method in single and multiple PV installations with inverter power factors of unity and 0.95 lagging. The results show that the GWO algorithm achieves stable and consistent convergence, with a maximum PV penetration rate of 87.99% and a system power loss reduction of 85.57% in the scenario involving three PV units on three busbars at a 0.95 lagging power factor. Furthermore, an inverter power factor closer to unity tends to reduce the maximum achievable PV penetration. The proposed approach also improves voltage profiles, reduces line loading, and enhances overall distribution system performance.

Adaptive Sliding Mode Control with a Nonlinear Sliding Surface for DC-Bus Voltage Regulation in a Renewable-Energy-Based DC Microgrid

2026-06-01T13:01:33+00:00

This study proposes an adaptive sliding mode control (ASMC) scheme incorporating a nonlinear sliding surface (NSS), denoted ASMC-NSS, for direct-current (DC)-bus voltage regulation in a renewable-energy-based DC microgrid. ASMC augments conventional sliding mode control (CSMC) through channel-wise switching-gain scheduling based on the integral absolute error (IAE), while the NSS introduces bounded, state-dependent scaling of the current-tracking surface. The gain schedule adjusts the switching authority as the accumulated tracking error crosses prescribed thresholds, whereas the NSS shapes the reaching dynamics to improve transient tracking and suppress overshoot. The controller is applied to a system integrating a wind turbine, a photovoltaic (PV) array, and battery energy storage. MATLAB/Simulink comparisons with CSMC and ASMC without the NSS show that ASMC-NSS reduces the current-tracking IAE by 90.5% and 87.3%, respectively, and achieves a current settling time of 0.054 s. It maintains the 500 V DC bus with a maximum overshoot of 0.28 V and a 0.02 s recovery time to the ±0.5 V band. Lyapunov analysis establishes asymptotic stability of the ideal inner current loops and uniform ultimate boundedness under bounded matched uncertainties.

Sorting Model using Robotic Arm with Image Processing

2026-04-20T04:36:24+00:00

This paper presents the design and implementation of a product sorting model using a robotic arm integrated with image processing techniques. The system consists of a conveyor belt, a vision module, and robotic manipulators that work together to identify and classify objects through a camera and computer vision algorithms that detect product characteristics. The robotic arm then performs the corresponding sorting operation according to product quality requirements. The hardware design includes the construction of the robotic arm, control circuits, and integration with actuators, while the software design focuses on developing image processing algorithms and communication between the vision system and the robot controller. Experimental results show that the system achieves an average size measurement error of approximately ±2 mm, a classification accuracy of about 95%, and an average processing time of 2–3 seconds per product. These results demonstrate reliable recognition and classification performance compared to some previous research models. The proposed model emphasizes the feasibility of combining robotic manipulation and computer vision for automated sorting tasks in industrial applications such as food processing, household tools, and medical instruments, while also serving as a practical training platform for students in technical education. Future improvements may include optimizing vision algorithms, enhancing the mechanical design of the robotic arm, integrating artificial intelligence to improve safety, and expanding the system’s capability to handle more complex classification tasks.

Image Processing-Based Morris Water Maze Rat Tracking System: Design and Analysis

2026-06-04T07:42:20+00:00

Four experimental rats consisting of one normal rat and three Alzheimer's-induced rats were analyzed. The proposed system employed ImageJ integrated with the RatsTrack and Macro RatsTrack plugins using a centroid based tracking approach. The system successfully extracted movement trajectories, distance travelled, speed, acceleration, and quadrant dwell time during acquisition and probe trials. The system performance was validated by comparing quadrant dwell times obtained from the proposed system with manual observations. The percentage differences ranged from 2.29% to 70.97%. demonstrating that the proposed system can effectively represent rat behavioral patterns, although discrepancies remain in certain quadrants requiring further refinement.

Adaptive Fixed-Time Nonlinear Integral Sliding Mode Control for Trajectory Tracking of Unmanned Surface Vehicles under Unknown Disturbances

2026-08-10T11:13:26+00:00

This paper proposes an adaptive fixed-time nonlinear integral sliding mode control scheme for trajectory tracking of unmanned surface vehicles (USVs) subject to nonlinear hydrodynamics and unknown time-varying environmental disturbances. The three-degree-of-freedom USV model is transformed into an inertial-coordinate second-order form, and an integral sliding variable is combined with two power-type reaching terms and a leaky adaptive robust gain that does not require the unknown disturbance bound in the control law. The Lyapunov analysis explicitly distinguishes the ideal and implemented controllers. For the saturation-based implementation, the sliding variable enters an explicitly characterized invariant neighborhood within a fixed time independent of its initial value, and the tracking errors are uniformly ultimately bounded. For the ideal sign-based controller, exact fixed-time sliding and asymptotic tracking are recovered under a separate disturbance-dominance condition. Comparative simulations for circular and straight-line trajectories include computed-torque proportional-derivative and backstepping-proxy benchmarks. For the straight-line trajectory, the proposed method obtains a total tracking-error RMSE of 0.1601, representing reductions of 64.9% and 54.3% relative to the CT-PD and BS-proxy benchmarks, respectively, with a settling time of 0.981 s. The saturation implementation also produces practically smooth control inputs.