Flooding Prevention in High Voltage Substations Using IoT Based System

Indonesia is a country with high rainfall. The rainy season can last for four months of the year. Development in urban areas in increasing resulting, resulting in reduced water absorption areas. People's habit of throwing garbage in waterways is also one of the factors that cause flooding. The current obstacle is the difficulty of monitoring water levels when heavy rains cause water to overflow onto roads and residents' houses. Checking the water level is still done manually, only looking at the measuring limit or ruler found on the river and not giving warning messages when the water level rises and it rains. Devices are needed to monitor water levels during heavy rains and send alerts to officers when it rains and the water level has exceeded the limit. Researchers found the idea to make a water level monitor and rain detector using the Wemos D1 R2 Mini microcontroller, supported by the HC-SR04 sensor and Internet of Things (IoT) based rain sensor. The Blynk application receives data from Wemos to be forwarded as a notification to officers. This system provides real-time information about river water levels and conditions when it rains to help avoid or reduce losses due to flooding. The accuracy value generated by the ultrasonic sensor reaches 99.89%, while from the rain sensor, it reaches 100%.

This study presents a novel power grid system, for monitoring and controlling a power grid that is made up of various power sources, by using Information and Communication Technologies ICT. This can be hugely contributed to improving the reliability, efficiency as well as towards making smart and automated power systems. The current model was developed in the MATLAB Simulink environment. The proposed system consists of a PV panel, battery, power grid, ICT system/controller, and loads. There are four insulated-gate bipolar transistor switches; namely, IGBT to control battery charging/discharging status and to connect or disconnect either PV panel power source or grid power source depending on the load. The main aim of this model is to develop and implement a grid-connected system that is integrated with PV panel and battery in MATLAB to analyze various operations modes, which include the performance of the PV panel and battery during both the online and offline modes of operation. The proposed system is said to be capable to monitor and control different power sources, which in turn will lead not only to reducing dependence on non-renewable energies but also to increasing dependence on renewable energies. The simulation results were illustrated and demonstrated that the proposed system can monitor and control the power generated and transmission in real-time in a better and secure manner. On the other hand, exploiting this model leads to reduce the power production costs, creating a clean environment, and also to preserve long term battery life.

A Flood is inevitable in some regions in Indonesia during rainy season. Flood can cause problems ranging from simple problem such as lightly flooded street to a complex one such as water that enters the house. For community living at river side, the flood often comes from excessive water due to rain in the upstream that outflows the river as drainage which can happen unnoticeably. An early warning system is needed to detect flood so that people who lived in a flood-prone area can be warned and be prepared to mitigate the flood. With the help of internet of things (IoT) technology, such a system can be made. In this work, an early warning system was developed using off-grid solar system as power source, ultrasonic sensor to detect water level, NodeMCU as microcontroller, and 4G modem to connect to the internet. The system uses Blynk as its IoT platform and sends alarm messages to locals using Telegram so they are vigilant. The system can detect how much the water level and categorizes it into preset alarm levels. Lab. experiments on the proposed system was performed using power from PV-module installed on the lab.’s rooftop. The system functionality works well for 3 weeks of experiments with battery voltage monitored to vary from 12 V at dawn to 13.7 V at daytime. The experiments show that the system exhibits water level measurement average relative error of 2.22% and accuracy of 97.78%. The system is also furnished with power management and auto restart self-healing functionality which were also observed to work well during the test. Measurements show that the system only consumes energy of 11.52 Wh/day which is equivalent to 8% of battery depth of discharge, hence the system can operate for 3.75 days without charging during successive rainy and cloudy days and has sufficient solar energy harvesting margin to cope with site imperfections like shading and soiling of the PV-module.

The excessive nutrient enrichment of water bodies, primarily from agricultural runoff, is a leading cause of eutrophication worldwide. The Enkare-Narok River in Kenya faces severe nutrient pollution; however, a quantitative assessment of specific nutrient losses is critically needed to inform effective mitigation strategies. This study aimed to (1) quantify the concentrations of nitrate (NO₃⁻) and phosphorus (P) during the single rainy season (April–May 2025); (2) analyze their spatial and temporal variations at the selected points. A sampling campaign collected 200 water samples from two points: Pumping Station and downstream RGS 2K03. A total of 200 water samples were collected: 100 from the Pumping Station and 100 from the RGS 2K03 site. At each site, 50 samples were allocated for nitrate (N) analysis and 50 for phosphorus (P) analysis. The selection of the two sampling points, Pumping Station and RGS 2K03, was strategic and guided by the GIS-based land use analysis to ensure they captured the dominant pollution pathways and cumulative impacts within the Enkare-Narok River Basin. The primary criteria for selection were representativeness of key land uses, position along the pollution gradient, and practical feasibility. The N and P concentrations were analyzed in triplicate using a calibrated HACH DR900 spectrophotometer following USEPA methods. A two-way Analysis of Variance (ANOVA) tested the main effects of sampling location and time on nutrient concentrations, and linear regression quantified the relationship between water level, nitrate and Phosphorus. The results exceed regulatory thresholds, with nitrate averaging 13.9 mg/L and phosphorus maximum at 2.5 mg/L. These levels pose serious risks: the mean nitrate concentration exceeds the WHO (10 mg/L) and KEBS (10 mg/L) drinking water thresholds, elevating health risks for vulnerable populations like infants (methemoglobinemia). The extreme total phosphorus level (2.5 mg/L), which is 25 times the WHOrecommended 0.1 mg/L eutrophication threshold, signals a high potential for toxic cyanobacterial proliferation and aquatic habitat degradation through hypoxia. Statistical analyses confirmed significant spatial and temporal variations, with nitrate concentrations significantly higher downstream and spiking sharply following rainfall events (p < 0.001). Regression showed a positive correlation (R² = 0.58, p < 0.001) between water level and nitrate, confirming that storm runoff is a primary driver of nutrient pulses. The significantly higher nitrate levels at the Pumping Station align with its position receiving cumulative runoff from upstream agricultural and urban anthropogenic activities from the city. This study, though based on a two-month rainy season snapshot, conclusively identifies agricultural runoff and urban discharges as the primary drivers of nutrient pollution threatening both ecosystem integrity and public health. This study underscores the critical need for targeted strategies to mitigate non-point source pollution and protect surface water quality. These include implementing riparian buffers to intercept subsurface flow, a key pathway delivering nitrate to surface water, and promoting farmer-led agroforestry to reduce surface erosion and phosphorus runoff.

Resilience theory plays a critical part in the practical design of modern engineered resilient systems that comprise of cyber‐physical systems and components having interdependencies intended to make the whole system robust. In this paper, the percolation‐based metric is utilized to quantify the communication systems' resilience. Because there is no consistent and reliable way to measure resilience, the percolation‐based metric framework is developed to model the component or system failure by removing fractions of the nodes or links, causing the network to transition from functional to non‐functional. The efficacy of the percolation‐based metric model is demonstrated by using the power grid and maritime platform systems (legacy and future design versions) as case studies. The results from the analysis of the maritime communication and power grid systems are presented. The results indicate that percolation‐based metric may be a useful approach for system engineers and designers to gain improved insight into the quantification of communication system resilience.

Seasonal changes and the contribution of industrial effluent discharges to the population of heavy metal – resistant bacteria (HMRB) in the river water and sediment of the New Calabar River, were examined. On exposure of river water microflora to 2µg of heavy metals, the HMRB population ranged from 55 to 78% in the rainy months and 100% in the dry months. The HMRB of the river water ranged from 0.4 to 0.8% and 11 to 21% in the rainy and dry months respectively, on exposure of river water microflora to 20µg of heavy metals. The HMRB in the sediment ranged from 86 to 100% and 20 to 43% in the dry months and 55 to 100% and 1.7 to 2.4% in the rainy months when sediment microflora were exposed to 2µg and 20µg of heavy metals respectively. Higher concentrations of heavy metals resulted in reduced populations of HMRB. The total aerobic heterotrophic bacterial (THB) count ranged from 1.0 x 107 to 5.8 x 108 cfu/ml and 2.2 x 106 to 5.3 x 107 cfu/ml for surface water, 5.5 x 106 to 7.4 x 108 cfu/ml and 1.4 x 105 to 4.0 x 106 cfu/ml for subsurface water and 1.0 x 105 to 1.1 x 109 and 2.5 x 105 to 6.8 x 107 cfu/g for sediment during the rainy and dry months respectively. Physicochemical parameters such as conductivity and BOD showed higher values in the rainy season than in the dry season. Dissolved oxygen and pH showed no significant difference between their rainy and dry season levels indicating the absence of seasonal variations with these parameters. Heavy metals levels of river water and sediment showed higher values in the dry season than in the rainy season. There was no significant difference between the heavy metal levels of industrial effluent discharges with those of the river water and sediment levels during the rainy season. During the dry season, the heavy metal levels of the industrial effluents were less than those of the river water and sediment. Thus their contribution to seasonal variation of these metals in the river water and sediment is negligible. Results also suggested that the percentage of heavy metal resistant bacteria and the heavy metal levels of the river water and sediments were higher in the dry season than in the rainy season. Key words: Seasonal changes, effluent discharges, heavy metal resistant bacteria, New Calabar River. (Global Journal of Pure and Applied Sciences: 2003 9(4): 425-434)

This paper presents a real-time remote water level monitoring system based on dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network for the application of the Internet of Things (IoT). This network employs a broadband light source based on amplified spontaneous emission (ASE) as a seed light. This ASE light is spectrum-sliced by an athermal type arrayed waveguide grating (200 GHz × 16 channel), then distributed towards multiple sensing units (SU). Here, 16 SUs are installed vertically at the specified height in the water pool according to the design specification (i.e., spatial resolution). Then, each SU reflects an optical spectrum having a different reflection coefficient depending on the surrounding medium (e.g., air or water). By measuring these reflected optical spectra with an optical spectrum analyzer, the water level can be easily recognized in real time. However, as the sensing distance increases, system performance is severely degraded due to the Rayleigh Back-Scattering of the ASE light. As a result, the remote sensing capability is limited at a short distance (i.e., <10 km). To overcome this limitation, we propose a simple signal processing technique based on feature extraction of received optical spectra, which includes embedding a peak detection algorithm with a signal validation check. For the specific, the proposed signal processing performs the peak power detection, signal quality monitoring, and determination/display of the actual water level through three function modules, i.e., data save/load module, signal processing module, and Human–Machine Interface display module. In particular, the signal quality of the remote sensing network can be easily monitored through several factors, such as the number of spectral peaks, the wavelength spacing between neighboring peaks and the pattern of detected peak power. Moreover, by using this validation check algorithm, it is also possible to diagnose various error types (such as peak detection error, loss of data and so on) according to the pattern of measured optical spectra. As a result, the IoT sensor network can recognize 17 different level statuses for the water level measurement from a distance of about 25 km away without active devices such as optical amplifiers (i.e., passive remote sensing).

Chili is a promising commodity with excellent business opportunities. However, chili cultivation has a big challenge since it cannot be harvested throughout the year and is vulnerable to weather and diseases. Several systems have been proposed to monitor chili cultivation. However, the proposed systems need a control system to give users access to the actuator. Hence, combining monitoring and the controlling system can obtain maximum chili cultivation results. The focus of this study is that the aquaponics system is developed with the Internet of Things (IoT) technology to control and monitor the condition of the chili growth, such as temperature, pH level, water level, nutrition, and humidity. The proposed system uses internet connectivity to measure the aquaponics condition remotely. Moreover, this system offers the user a feature that can control the system directly through the mobile application. The sensors in the proposed system also have great measurement ability with a total average accuracy of 96.29%. Thus, it indicates that the proposed system has excellent performance.

Water level prediction is an essential factor for the safe operation of pumping stations. However, due to the complex nonlinear relationship between the water level of the front pool of the pumping station and the influencing factors, the prediction of the water level is still inaccurate and untimely. Backpropagation (BP) neural network, improved particle swarm optimization-BP neural network (PSO-BP), support vector machine regression (SVR), and improved PSO-SVR were used to construct 4-h and 8-h ahead prediction models for pumping station prestation water levels. Mean absolute error, mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R correlation coefficient were used as prediction evaluation metrics. This method is applied in the Baliwan Pumping Station, the highest pumping station in the South-to-North Water Diversion Eastern Route Project (SNWDERP). The results showed that the MSE, RMSE, and MAPE of the improved PSO-BP model were smaller than other models, whereas the R correlation coefficient was larger, confirming its high prediction accuracy. All models had higher prediction accuracy 4 h ahead than 8 h ahead. Combining the time-phased water level prediction method and hybrid machine learning can enhance the water level prediction accuracy of a pumping station pre-station.

The Internet of things (IoT) plays important roles in the modern digital world. It has several important roles in the modern power systems and power grids. IoT presents a lot of potential in the power systems and power grids. Some of the support functions are direct and several others are found to be indirect. Either way, IoT can play a lot of important roles in the modern power systems. It can help significantly in the measurement, control, and monitoring of the physical parameters in the power grids. It helps to in the reduction of energy consumption in the power electronic components. It has the potential to provide a lot of operational flexibilities in the power electronic components. Implementation of advanced operational algorithms using artificial intelligence and machine learning is facilitated by the IoT based sensors, actuators and other key components. It can provide smart operational assistance to the power electronic systems used in the power grids. Due to their logical flexibilities IoT sensors can be deployed alongside the power electronic components to track their performances. Consequently, using the IoT sensors' information, the actuators are driven to deliver optimal outcome. IoT sensors' information can be sent directly to the central servers in regular intervals to monitor the overall performances of the power electronic components. In addition to the aforesaid applications, several other potential uses of IoT in power electronics include monitoring of critical power grid parameters such as temperature, current, voltage and vibration at different key locations. In this paper, we analyze the use of IoT in power electronic components in the modern power systems.

Electric Vehicles (EVs) have become a viable choice for cutting carbon emissions in the field of road transportation. EVs will be incorporated into the electrical grid at an increasing rate in the near future. EVs themselves are becoming immense power consuming load on the power grid system as a result of the growing number of EVs, mostly light-duty EVs. In addition to posing major technical risks such power quality issues, voltage swings, harmonic injection, battery deterioration, and grid instability, the unplanned installation and rapid adoption of charging stations might impede the smooth functioning of the power distribution system. Therefore, this paper analyzes and summarizes the potential issues and solutions for Fast Wireless Charging (FWC) that allows for the wireless transmission of very high-power levels (>20 kW) from the power grid to explore the impact of EV charging on the power system network. This need can have far-reaching effects on the load profile, operation, and design of the distribution network. In order to avoid exceeding the grid's technical restrictions, it is crucial to find the maximum feasible deployment level before rolling out an FWC network. This study provides a method for assessing how FWC technologies influence distribution networks. Using a realistic model of an Indian MV distribution feeder, the proposed methodology is used, providing both quantitative and qualitative results that serve as potential directions for future research studies.

In this paper, we bring about the idea of water level monitoring and controlling with IOT and Mobile applications. The vast amount of water wasted in the current scenario, mostly due to overflowing tanks is not acceptable. Existing water tanks control systems can monitor and control the water level in tank, which leads to reduction in amount of wastage of ample water but the type of sensors which are used in such a system does not specify exact changes in water level leading to unstable control parameters. Other technologies had some drawbacks based on speed of detection and approximation of sensor data. The need of improvement of these short-comings and providing an accurate and ethinic solution has been the main aim of this project. The project has been further improvised by using electronic water level sensors which uses potentiometric techniques to measure water level in water tanks, along with ultrasonic sensors for better metrics and control. Further up, this project can be implemented on large scale to control and detect rising water levels in dams and reservoirs to avoid flash floods and excess pressure in dams. The proposed system will help to identify the smallest changes in water level in case of rainfall measurement as well.

Monitoring water levels in reservoirs and suction pipes is essential to avoid failures in pumping and agricultural systems. This study aimed to develop an intelligent system for automated monitoring of water levels in irrigated agricultural reservoirs, ensuring safer operation of pumping stations and aiding decision-making. The system enables visualization, water level management, and protection of motor pumps. After assembly and programming, the prototype was installed in a reservoir. Tests showed a difference of less than 5 mm between manual and system measurements. Google Sheets was used for accurate data analysis. The control system includes a contactor, thermal relay, bipolar circuit breaker, simple control relay (activated by the ESP32), and two buttons — one normally closed and one normally open. The proposed intelligent system proved effective for automated water level management in reservoirs.

Floods that come all of a sudden cause many people to be unable to prepare themselves to deal with it, so material losses to health problems cannot be avoided. Therefore, a system is needed to provide early warning to the public before a flood occurs. As technology develops, water levels in an area can be monitored to anticipate flooding using the Internet of Things (IoT). IoT can help to monitor and warn of floods in real-time and continuously. In this research, the system will be placed in areas that often cause flooding. Monitoring results from sensor readings will be stored in the cloud database. The water level category is divided into 4 levels, namely “Safe”, “Standby”, “Careful”, and “Danger”. The system uses IoT and a database to send water level status to users as notifications on applications. The buzzer will sound as a warning sign when the water level enters the “Danger” status. The system test results show that the sensor has a very good level of accuracy with an error percentage of 0.242131%, and IoT connectivity can reach a distance of up to 20 meters.

Peatland cover over 400 million hectares of the Earth surface. It stores massive amount of carbon pool. They play major roles in accommodating the Earth global warming. But the logging activities from development agriculture site, and housing, has disturbed the ecosystem of peatlands habitat. Thus, urgent action from worldwide is required to protect and restore the peatlands. This includes raising the peat surface water level to avoid drying to mitigate fire. Canal blocking was done as one of the strategies to raise it. However, too high-water level might contribute to another problem, flooding. Hence, continuous monitoring is required to ensure the water level is maintained within the optimum level. This project explains the proposed system for water level detection on peatland areas using visual Internet of things (IoT). In addressing the power consumption issue a mechanism to enable power to be used only when necessary was introduced. This mechanism will only awake the camera when a certain threshold is reached and triggered the camera to capture the image. The proposed system includes the use of Raspberry Pi and camera as device to capture image with integration of Arduino to acquire sensors reading. The image is then processed and finally transmitted over cloud network. The outcome of this project produced camera-based water level detection prototype. The scope for this project is to highlights on Brunei peatland areas located at Badas, Brunei.