The shore power cable is directly connected between the ship and the shore. Affected by direct sunlight, sea wind and tide, the shore power cable’s insulation has aged, which has a significant impact on the shore power cable’s service life and current carrying capability. In order to investigate the effects of wind speed, water flow velocity, solar radiation, and ambient temperature on the temperature distribution of shore power cables, the paper builds an electromagnetic-thermal-flow multiphysics simulation model of shore power cable CEU94 - 8.7/10 kV - 3×120 mm ² . Each layer of the model takes into account parameters such as thermal conductivity and electrical conductivity of the material and is based on the characteristics of the finite element method to ensure accurate calculation of the temperature distribution. Meanwhile, the boundary conditions such as wind speed and water flow velocity are based on meteorological data and water current measurements of the actual harbor to ensure the technical rationality of the model. The findings demonstrate that the conductor temperature varies in a linear fashion with the surrounding temperature. There is a 10°C increase in the ambient temperature and an 11.66°C increase in the conductor temperature; When the wind speed increases from still to 10 m/s, the maximum conductor temperature decreases by 13.84°C; When the shore power cable is laid in the air, the decrease in temperature is within 2°C when the water speed is 10 m/s, and the decrease in temperature is within 15°C when the wind speed is 10 m/s. When laid in water, the temperature stabilizes with the flow velocity, and the temperature of laying in water is about 5.5°C lower than that of laying in air; In extremely hot summer weather, the shore power cable’s maximum conductor temperature is 117.6°C.

It is difficult to consider factors such as wind speed, water flow velocity, and solar radiation when using the IEC 60287 standard to calculate the current-carrying capacity of shore power cables in port microgrids. Therefore, based on the equivalent thermal circuit model and heat balance equation, this research takes solar radiation as the heat source of the cable used in port microgrids and proposes a mathematical calculation method for the current-carrying capacity of shore power cables based on the Newton–Raphson method. The influence of wind and water speed, environmental temperature, and solar radiation on current-carrying capacity is compared and analyzed using this mathematical calculation method and simulation calculation method. Shore power cables exhibit higher ampacity in water than air due to water’s superior thermal conductivity. Maximum ampacity difference occurs at 0.17 m/s flow (26.8 A analytically) and 0.066 m/s flow (64.4 A simulation). Air-laid cables show amplified ambient temperature effects from solar radiation, while water-laid cables demonstrate near-linear ampacity variations (Δ40 °C: 0–40 °C temperature range). This research can provide a reference for the revision of the standard for calculating the current-carrying capacity of shore power cables and optimizing renewable-energy-integrated port power systems.

The impact of excessive harmonic distortion on electrical distribution networks highlights the critical need to understand harmonics in different lighting technologies to detect sources of issues and their effects. The increasing prevalence of nonlinear loads in power systems has contributed to significant harmonic pollution, deteriorating power line quality. This paper aims to address this issue by focusing on power line frequency detection and the classification of lighting technologies, combining empirical mode decomposition (EMD) with the one-versus-one support vector machine (OVO-SVM) approach. EMD is utilized to analyze signals through intrinsic mode functions, preserving the signal’s characteristics while extracting features related to harmonic distortion from various lighting types, including light emitting diodes, compact fluorescent lamps, and incandescent bulbs. The integration with MATLAB/Simulink and an Arduino Uno enables real-time monitoring, and the use of quantization and FFT implementations measures total harmonic distortion. In terms of classification, the OVO-SVM using polynomial kernel functions outperforms the RBF kernel functions across various training-to-testing splits (50/50, 60/40, 70/30, and 80/20). Additionally, the performance of the OVO-SVM with polynomial kernel functions aligns closely with the gradient boosting machine classifier, showing identical results in accuracy, precision, recall, and F1-score. This suggests that both models are highly effective in classifying the types of harmonic distortions associated with different lighting technologies, offering an efficient method for power quality disturbances detection and classification in power systems.

Internet of t hings (IoT) based floor cleaning robot (FC - Rob) is a floor cleaning robot that uses a smartphone to assist users, primarily housewives and mothers, in completing their chores. The NodeMCU ESP8266 serves as the robot’ s “brain” and is controlled by a smartphone application for the purpose of this research. In accordance with current trends, a n iOS and Android application using Blynk has also been developed for users to control the robot’ s movements, making it the ideal solution for time - crunched individuals. FC - Rob is propelled by two d irect current (DC) motors to ensure comprehensive floor cl eaning. The results of this research are strengthened by tests conducted on battery life and two types of fabric, as well as a comparison with two types of commercially available robots. The positive findings of these tests on this robot demonstrate its effectiveness and efficiency in cleani ng houses, as well as its reasonable cost and educational value for children.

Power disturbances monitoring is one of the important aspects on dealing power quality issue in electrical system. The aims of conducting monitoring process are to identify the real culprit which contribute to the Power Quality (PQ) problem. One of the vital steps during monitoring process is the classifying various type of power disturbance. This classification process is very important to give a right direction towards proposing the correct mitigation technique. In order to produce reliable classification technique, the devices which has a flexibility on accommodating the software and hardware part need to be deployed. The software is need for algorithm development such as signal processing, Artificial Intelligent (AI) as well as statistical analysis. On the hardware part, the device’s ability to acquire the electrical parameter within the electrical system operation is very important. The data acquisition based on the voltage and current is essential to be feed in the classification algorithm in software side. On the other hand, the interfacing devices and data acquisition module need to be developed at the hardware side, LabVIEW manage to accommodate both software and hardware need and further development of the LabVIEW for this purpose will be elaborated in this chapter.