Articles

Analysis of Harmonic Improvement in the Computer Laboratory of Samarinda State Polytechnic with the Addition of a Static VAR Compensator

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An electrical system is said to have a high level of reliability if it is capable of supplying electrical energy continuously and in accordance with user needs. The quality of electrical power can be disrupted by the use of non-linear loads, such as computer devices, electronic devices, and energy-saving lamps. Harmonic problems in power quality arise due to the interaction between the sinusoidal waves of the system and the characteristics of non-linear loads, such as those found in the Computer Laboratory of the Samarinda State Polytechnic, which results in high harmonic current values. To determine the actual conditions, electrical parameters were measured. The results of the measurements showed a total current harmonic distortion value of 33.65% in phase R, 15.85% in phase S, and 92.99% in phase T. The individual current harmonic distortion in phase R decreased from 28.39% to 4.53%, to 3.76% in phase S, and to 2.63% in phase T on Thursday at 15:00 WITA. To overcome this, the use of a Static Var Compensator (SVC) is proposed as a solution to reduce the level of harmonics in the electrical system at the Computer Laboratory of the Samarinda State Polytechnic. The simulation was conducted using Matlab R2023a software through the Simulink feature. The results obtained show that the Static Var Compensator can reduce harmonics well, as seen from the harmonic values on Thursday, where before the Static Var Compensator was installed, the harmonics in phase R were 9.81%, phase S were 10.98%, and phase T were 9%.

Optimization of Wireless Mesh Networks for Disaster Response Communication

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Wireless Mesh Networks (WMNs) have emerged as a resilient and adaptable solution for disaster response communication, offering self-healing and self-organizing capabilities that ensure uninterrupted connectivity in emergency scenarios. Traditional communication infrastructures often fail due to network congestion, power outages, and physical damage during disasters, necessitating an optimized approach for rapid and reliable data transmission. This study presents an AI-optimized WMN framework aimed at enhancing network performance by improving packet delivery ratio (PDR), reducing end-to-end delay, optimizing energy consumption, increasing network throughput, and strengthening security. Simulations conducted in MATLAB Simulink compare the performance of AI-optimized routing with conventional protocols such as AODV (Ad hoc On-Demand Distance Vector) and OLSR (Optimized Link State Routing). Results demonstrate that AI-optimized routing achieves a 15.5% higher PDR, 43% lower delay, 49% increased throughput, and 30% reduced energy consumption compared to traditional approaches. Furthermore, an AI-driven Intrusion Detection System (IDS) improves network security by increasing attack detection accuracy to 94.6% while reducing false positive rates to 5.2%. The findings highlight the significance of AI-based routing optimization in disaster scenarios, ensuring robust, energy-efficient, and secure communication for first responders and affected communities. Future research will explore hybrid AI-blockchain security mechanisms, 5G and satellite network integration, and real-world experimental validation to further enhance WMN resilience in extreme disaster conditions.

Power Factor Improvement in the New Civil Engineering Building at State Polytechnic of Samarinda

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The State Polytechnic of Samarinda is grappling with reactive power issues, primarily stemming from the considerable number of electrical loads, including computers, fluorescent lamps, printers, air conditioners, and electric motors, present in its buildings and laboratories. As a solution, the installation of a static var compensator (SVC) is proposed to enhance the electrical power factor at the Samarinda State Polytechnic, with a specific focus on the new Civil Engineering Department building. To assess the impact on the power factor, simulations were conducted using MATLAB R2021a Simulink software. The findings reveal that the utilization of a static var compensator resulted in an average power factor increase of 25% across all experiments. However, the targeted power factor of 0.99 was not attained. Furthermore, employing the SVC led to a reduction in current in the R phase by an average of 1.8%, in the S phase by an average of 35%, and in the T phase by an average of 37%. Concurrently, there was an average increase in active power by 3.5%, while apparent power decreased by an average of 14%, and reactive power decreased by an average of 74%. Despite encountering some limitations, the implementation of SVC proved successful in enhancing the power factor in the simulation, presenting a viable solution for improving power quality in the buildings of Samarinda State Polytechnic.