In recent decades, the demand for electricity has continuously increased. Power generation facilities are predominantly situated at substantial distances from consumption centers, necessitating transmission over extensive, high-voltage lines. Such configurations lead to significant energy losses and diminished capacity and capability of transmission systems. Consequently, enhancements in transmission line performance have become a focal point for power system operators. The integration of the flexible alternating current transmission system (FACTS) technology has emerged as a pivotal solution, facilitating dynamic control over power flow and amplifying the existing capacity of power lines without the need for constructing new infrastructure. Among various FACTS devices, the static synchronous series compensator (SSSC) plays a crucial role by injecting variable capacitive or inductive reactance as required, thereby optimizing power flow and enhancing voltage stability. This review paper meticulously examines the functionality of different FACTS technologies, with a specific focus on the SSSC. Comparative analyses of transmission line performance, uncompensated, compensated through traditional series capacitors, and enhanced via SSSC, were conducted. The findings underscore the versatility of SSSC in reducing transmission losses and stabilizing network operations. This investigation not only details the operational benefits of SSSC but also explores its potential in addressing contemporary challenges in power transmission systems.
This study evaluates the usability and effectiveness of a newly developed special education (SPED) campus navigation system designed for students with visual impairment (SVI) at the Federal College of Education (Special), Oyo, Oyo State, Nigeria. The primary objective was to assess the system's capability to facilitate self-navigation for SVI and identify challenges encountered in a campus environment. A mixed-methods approach, combining quantitative data from questionnaires and qualitative insights from interviews, was employed. Twenty SVI, selected through purposive sampling, participated in the study, using the system over a five-week period. The findings indicate significant improvements in the orientation and mobility of SVI, resulting in increased confidence in navigating the campus. Participants reported that the navigation system effectively aided in locating key areas, detecting obstacles, and ensuring safety. However, several critical challenges were identified, such as the system's voice being drowned out in noisy environments and the frequent need for battery recharging every five days. Participants suggested enhancements, including the incorporation of volume control to accommodate various environmental conditions and regular device charging to prevent battery depletion. These improvements are deemed essential for enhancing the system's reliability and usability for SVI.
Regulatory bodies in electrical engineering mandate the installation of power factor (PF) improvement systems to elevate PF values to between 0.9 and 0.96. Compliance is enforced by regional or local utility companies through penal rates and incentives for PF values nearing unity. Traditional power factor correction (PFC) systems often utilize microprocessor-based controllers for switching capacitor banks, which can result in under- or over-compensation of reactive power. This study developed an adaptive neuro-fuzzy inference system (ANFIS) utilizing a Sugeno-Takagi inference model based on the sub-clustering method to address the limitations of sensitivity and response time observed in existing microcontroller-based PFC systems. The proposed neuro-fuzzy (NF) controller comprises a five-layered model with two inputs, i.e., kilowatt (KW) and kilovolt-ampere reactive (KVAR), and one output (PF). A 25-rule set performance of the developed program was achieved, with significant improvements observed after 50 epochs, culminating in an error rate of 0.050691 recorded post the second epoch. The results demonstrated that the developed controller exhibits higher sensitivity and faster response time compared to existing PF controllers. Consequently, the implementation of the proposed controller is recommended for optimizing the switching of capacitor banks, thereby enhancing PF in manufacturing industries characterized by variable load conditions.
Separately excited direct current (DC) motors, renowned for their linear characteristics and controllability, are extensively employed in various industrial applications. Effective speed control of these motors can be achieved through multiple methods, with fuzzy logic being a particularly robust approach. This study focuses on evaluating the transient responses of current and voltage in relation to the rotational speed of a DC motor under two distinct control schemes: field control and armature control, both subjected to similar load disturbances. A simulation-based methodology was employed using a DC motor speed control system combined with a fuzzy logic controller (FLC) designed with the Mamdani min-max method. The system was implemented in Simulink. In this framework, the FLC processes speed error signals and field current ($I_f$) errors as inputs to generate a field voltage control signal, which is then utilized by the armature voltage (Va) regulator to modulate the armature voltage. The results demonstrate that the FLC effectively stabilizes motor speed, quickly and accurately following speed references, even under load disturbances. Moreover, the system effectively mitigates speed fluctuations induced by load variations. A comparison between the two control schemes reveals that the field control approach exhibits a slower response time, taking 2.93 seconds to reach a steady state, whereas the armature control achieves this in a significantly faster time of 0.144 seconds. These findings underscore the efficacy of fuzzy logic in maintaining stable and responsive speed control in DC motors, with the armature control method displaying superior transient performance.
The recognition of abnormal events in surveillance video streams plays a crucial role in modern security systems, yet conventional techniques such as Support Vector Machines (SVMs) and decision trees (DTs) exhibit limitations in terms of accuracy and efficiency. These traditional models are often hindered by high false alarm rates and struggle to adapt to dynamic environments with variable conditions, thus reducing their practical applicability. In response to these challenges, an innovative approach, termed Adaptive Regression for Event Recognition (ARER), has been developed, leveraging advanced regression algorithms tailored for video data analysis. The ARER model integrates deep learning techniques, allowing for more precise temporal and contextual analysis of video footage. This methodology is structured through a multi-layered architecture that progresses from basic motion detection to complex anomaly identification. Trained on an extensive dataset covering a range of environmental and situational variables, ARER demonstrates enhanced robustness and adaptability. Evaluation results indicate that the ARER model achieves a 0.35% improvement in detection accuracy and a 0.40% reduction in false positives when compared to SVMs. Additionally, system throughput is increased by 0.25%, and detection latency is reduced by 0.30% in comparison to DTs. These advancements highlight the ARER approach as a superior alternative for real-time monitoring, offering significant improvements in both reliability and performance for surveillance applications.
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