To address the issues of lack of rotational inertia and deterioration of frequency dynamic characteristics of the power system caused by the large‐scale grid connection of new energy equipment, an adaptive inertia model predictive frequency regulation control strategy is proposed. The strategy firstly constructs an adaptive moment of inertia containing angular frequency deviation and angular frequency rate of change by introducing an inverse tangent function based on the frequency regulation mechanism of a virtual synchronous generator (VSG). Then, a model predictive control (MPC) objective function is established with system angular frequency deviation and VSG‐rated power variation as variables, incorporating a dynamic weight adjustment mechanism for frequency deviation. Furthermore, the calculated active power correction value is used to compensate the input active power in real time, thereby optimizing frequency dynamics. Experimental results demonstrate that the proposed strategy effectively reduces the maximum frequency deviation and its rate of change.
This paper proposes an antidisturbance backstepping control method based on an independent reference model to address the trajectory tracking problem of single‐joint manipulators subject to external disturbances. First, a nonlinear dynamic model of the manipulator is established, and a basic backstepping controller is designed to derive the independent reference model. On this basis, a generalized state error vector is constructed, and an antidisturbance backstepping controller is developed via Lyapunov stability theory to realize dynamic disturbance compensation. The global asymptotic stability of the closed‐loop system is strictly proved through Hurwitz stability analysis. Simulation results demonstrate that under pulse, step, and continuous sinusoidal disturbances, the proposed method significantly outperforms traditional backstepping control in terms of robustness, effectively suppresses angular position deviation and oscillation phenomena, and achieves high‐precision trajectory tracking.
Precise satellite antenna control is essential for maintaining reliable communication links and signal accuracy. However, selecting an appropriate controller and tuning its parameters remain challenging because controller characteristics and parameter combinations strongly influence transient and steady-state performance. This study compares proportional (P), proportional-integral (PI), and PI-derivative (PID) controllers for azimuth-angle control of a satellite antenna to achieve accurate, stable, and rapid positioning. First, the transfer function relating the input command to the antenna position is derived, and the stability and performance of the uncontrolled system are evaluated. The Ziegler–Nichols method is then used to obtain initial controller parameters, followed by MATLAB-based tuning to improve performance. Step-response results are presented for different controller settings. Among the conventional controllers examined, the PID controller with proportional, integral, and derivative gains of 200, 100, and 150, respectively, provides the best overall response, with short rise and settling times and no overshoot. A genetic algorithm (GA) is subsequently applied to further tune the PID controller using two objective functions: a linear-quadratic regulator (LQR)–based cost function and the integral of time-weighted absolute error (ITAE). For this model, the GA–ITAE formulation yields substantially faster transient performance than the GA–LQR formulation, although it introduces a modest overshoot.
Miniature neutron source reactors (MNSRs) such as the Ghana Research Reactor-1 (GHARR-1) were designed in the 1980s and lack the modern automation and continuous monitoring capabilities now required in nuclear safety engineering. Post-Fukushima safety reforms and IAEA INSARR assessments highlighted a major deficiency across MNSRs: the absence of offline monitoring and the lack of an integrated control platform for auxiliary systems. At GHARR-1, shutdown procedures disable all monitoring functions, limiting diagnostic capability and creating a safety gap during nonoperational periods. This study addresses these challenges by developing an independent and integrated monitoring and control system for GHARR-1 auxiliary units. Using industrial process-modeling techniques, programmable logic controller (PLC)-based automation, LOGO!Soft Comfort, and a Siemens WinCC SCADA interface, the study models auxiliary subsystems, implements real-time data acquisition, and integrates alarms, trend analysis, and controlled user access into a unified HMI. Functional block programming and structured I/O design ensure robust communication between PLC logic and HMI visualization. Validations demonstrated a strong correlation with the reference GHARR-1 data. The resulting system provides continuous online and offline monitoring of temperature, control rod position, coolant chemistry, radiation levels, water levels, auxiliary pumps, and access control. It resolves the IAEA-identified offline-monitoring limitation, enhances situational awareness, supports predictive diagnostics, and aligns GHARR-1 with modern instrumentation and control standards. Its modular design also offers a scalable template for upgrading similar research reactor facilities.
This paper presents an advanced method for detecting physical anomalies in power systems using convolutional neural networks (CNNs) applied to time-series voltage measurements and their corresponding finite Fourier transforms (FFTs). The approach utilizes data generated by phasor measurement units (PMUs) in real time, focusing on the detection of anomalies caused by contingencies, such as faults, generator outages, or power line trips. We derive an analytical solution to the swing equations that models the behavior of voltages during disturbances and illustrate how these dynamics are represented in both the time and frequency domains. By combining voltage time-series data with FFTs in a structured format, we train a CNN model to distinguish between normal operation and various types of physical anomalies. The model captures spatial and temporal patterns in the data and utilizes the rank values of the voltage and FFT matrices as features for classification. The results show that the CNN model is effective at detecting anomalies in power systems, even in the presence of high-frequency data and complex system dynamics. This methodology provides a robust tool for real-time monitoring and can be used to prevent cascading failures or large-scale blackouts in power grids.
