Power transformer performance is strongly influenced by reactive power (VAR), yet the quantitative link between VAR fluctuation and insulation loss-of-life in heavy industrial systems remains under-documented. This study analyzes that link for six 70 kV power transformers (three at the Tonasa IV and three at the Tonasa V substations, 31.5–40 MVA) in the electrical system of PT Semen Tonasa, combining SCADA operational data, an ETAP load-flow simulation, and an analytical ageing-rate calculation based on IEC 60076-7. Varying reactive power from 1 to 10 MVAR at a fixed active power of 10 MW increased the loading level from 31.41% to 44.19% of the 80% operating limit, consistent with S = √(P²+Q²). SCADA records showed current rising and primary-side voltage falling as VAR increased, with all voltages (66.68–68.95 kV) remaining within ±10% of nominal. For the three Tonasa V units, hotspot temperatures of 65.42–75.23°C and loss-of-life rates of 0.022–0.069 pu/day were obtained, well below the 98°C/V = 1 design reference; Transformer #1 aged roughly 3.1 times faster than Transformer #2, reflecting its higher average loading (63.4% vs. 36.5% of the operating limit). The extrapolated remaining-life figures (79.7–250 years) are indicative rather than literal, given the short (3–5-day) SCADA window; the loss-of-life rate is therefore treated as the primary, more defensible indicator. Reactive-power compensation via capacitor banks or static var compensators is recommended to limit current, hotspot temperature, and insulation ageing.
Indoor air quality (IAQ) is a critical environmental health factor responsible for millions of cases of respiratory diseases each year. This study presents the design of an Internet of Things (IoT)-based indoor air circulation control system that integrates a BME680 sensor, an ESP32 microcontroller, and the Firebase Realtime Database platform with the Tsukamoto fuzzy logic algorithm as a control mechanism. This research implements a dual-core architecture on the ESP32, separating IoT communication tasks from sensor computation. This approach enables the BSEC algorithm to run consistently without interruption. The system uses two input variables (IAQ values and room temperature) as well as two actuators—a fan and a 12V DC blower—controlled via Pulse Width Modulation (PWM) signals based on 12 fuzzy rules. Testing was conducted in a miniature test chamber measuring 23.5 × 16 × 18 cm with controlled pollutant introduction. The research results show that the BME680 sensor has an average accuracy of 99.3% for temperature and 98.0% for humidity. All 12 fuzzy testing scenarios met the rule base criteria. The system was able to reduce the IAQ value from a very dirty condition (IAQ = 500) to a clean condition (IAQ ≤ 80) in an average time of 281 seconds. Data synchronization between the local LCD display and the Firebase-based mobile application achieved 100% accuracy. These results demonstrate that the Tsukamoto fuzzy method is effective in producing rule-based, stable, and proportional control for indoor air circulation systems.
This study was conducted to develop and test an Internet of Things (IoT)-based automated irrigation and lighting system for tomato plants using the Tsukamoto Fuzzy Logic method. The system utilizes an ESP32 microcontroller, with data collected from a CJMCU-TEMT6000 light sensor and a soil moisture sensor, which is then used to control a water pump and UV LED lights via PWM. Monitoring and control can be performed directly via the Firebase platform and an Android app. Test results show that the light sensor has an average error rate of 1.83% with an accuracy of 98.15%, and the soil moisture sensor has an average error rate of 2.15% with an accuracy of 97.85%. The IoT system responds stably with a latency ranging from 3.32 to 5.03 seconds. A 5-week experiment on three plants showed that the natural environment treatment exposed to direct sunlight (Plant C) resulted in stable vegetative growth, while the terrace treatment without direct sunlight exposure (Plant B) triggered rapid but inconsistent initial growth. Meanwhile, the special treatment combining irrigation and grow lights (Plant A) proved most effective in supporting optimal fruit development compared to the other treatments.
Indonesia is a country that is highly vulnerable to earthquakes due to its location along active tectonic plate boundaries. The existing problem is the limited availability of earthquake detection systems capable of providing fast and real-time information and early warnings to the public. This study aims to design and implement an ESP-32 based embedded system for earthquake detection using the MPU-6050 sensor. The developed system utilizes the MPU-6050 sensor to detect vibration acceleration changes processed by the ESP-32 microcontroller, with monitoring results displayed through a web dashboard and sent via a Telegram Bot in real-time. The research methods include hardware design, software development, system reliability testing, and power consumption analysis. The results show that the system is capable of detecting normal, alert, and earthquake conditions based on total acceleration values. The alert condition is within the range of 10.5 m/s 2 < at ≤ 12.0 m/s 2 , while earthquake conditions occur when at > 12.0 m/s 2 . In addition, the system achieved an average value of 10.17 m/s 2 and a standard deviation of 0.0256 m/s 2 , indicating that the MPU-6050 sensor readings have very small data deviations. Therefore, the system is capable of providing real-time earthquake monitoring and early warning effectively.
In a cloud-only Internet of Medical Things (IoMT) system, every urgent health alert must cross the wide area network twice before a clinician sees it. Those two crossings are costly. Under moderate load they use up most of the clinical time budget, and under heavy load most alerts arrive too late to be useful. A fog communication framework is presented here that addresses the problem by moving the alert decision to a gateway near the patient and by giving alert traffic strict priority over routine telemetry. The priority scheme is not a new algorithm. It is the standard three band PfifoFast queue of the Linux traffic control layer, so it can be deployed today on ordinary hardware. A single analytical model joins a log-distance wireless link, a Cobham non-preemptive priority queue at the gateway, and a hard alert deadline, and is solved in closed form for end to end latency, deadline-meet ratio, and backhaul saving. Three independent evaluators are then used: the analytical model, a 25-seed discrete event simulation, and a packet-level NS-3 simulation of the IEEE 802.15.4 access tier running the same PfifoFast queue. Traffic comes from a real vital signs dataset of 200 020 records, whose measured risk balance sets the critical and routine traffic mix. At an offered load of 0.90 the proposed design cuts mean critical-alert latency by 84.0%, from 151.7 ms to 24.2 ms, and raises deadline compliance from 60.6% to 100% against a cloud-only FIFO baseline. A paired Wilcoxon test confirms the difference (p = 5.96 x 10^-8). A factorial ablation shows that fog placement and priority scheduling help for different reasons, and that both are needed. Compared against Earliest Deadline First, which is optimal in theory, the simple stock queue reaches the same deadline reliability at far lower implementation cost. A transient test in which the critical share of traffic rises sixfold shows the guarantee still holds. Backhaul traffic falls by 95.3% at the same time.
