Thermal expansion and contraction of electronic component materials can be caused by extreme heat or cold. Plastic casings and metal components undergo thermal expansion and contraction, resulting in changes in their dimensions and shape in response to variations in temperature. These variations may impose mechanical stress on the parts, which can cause damage or poor connections in the circuit. The aim of this project is to set up an environment where electronic parts can be stored safely. Since changes in temperature and humidity may harm electronic parts and modules, this project aims to keep monitor and maintain the best conditions within the storage box. The project consists of up of four parts: an Arduino Uno R3 microcontroller, temperature and humidity sensor (DHT11), a buzzer, and a 16×2 LCD. Arduino is like the circuit's brain. The LCD is employed to display the current temperature and humidity values inside the storage box. This warns the user of potential electronic component damage. Overall, this project can help ensure the longevity and reliability of stored electronic components, reducing the likelihood of defects and preserving their performance over time.
This paper presented a dissolved gas analysis (DGA) based prediction of transformer health index (HI) for transformer fault determination and diagnosis over the life of power transformers. The transformer health index was invented and has been used in the industry for a long time. However, it lacks an accurate model and calculation because the transformer parameters are complex and the calculation needs to be validated by experts. Therefore, the DGA prediction algorithm to determine the health of the transformer is needed to solve this problem. In this project, gap analysis, DGA based health index calculation and performance analysis were used to determine the transformer health condition and the best ANN algorithm to predict the transformer HI. The study shows that Bayesian Regularization is the most effective compared to other prediction algorithms. In conclusion, the study shows that the proposed DGA prediction is feasible for further applications in predicting the transformer HI instead of relying on complex computation and expert validation.
The dynamic interaction between lighting and photovoltaic (PV) performance has significant implications for both indoor and outdoor PV system design and optimization. As a result, the purpose of this work was to analyze the performance of a single diode model of a photovoltaic (PV) cell, and the work was carried out in a solar system simulator in MATLAB. The effects of illumination were modified in MAT LAB for both indoor (2001x, 4001x, 6001x, 8001x, and 10001x) and outdoor (200, 400, 600, 800, and 1000 W/m2) illumination conditions using Solarex MSX 60 PV panels. For both conditions, the higher the irradiance, the higher the output current, and thus the higher the power generated. When comparing outdoor and indoor conditions, it may be stated that outdoor conditions are preferable to indoor conditions.
This study proposes a robust control strategy based super twisting-sliding mode direct power control. This latter has been applied at the two-level voltage Source inverter associated with a parallel active power filter (SAPF) withe space vector pulse width modulation (DPC SVM). This strategy to ensure the following objectives: obtain low total harmonic current, robustness, stabilization, reduce response time, ripple, and maintain fixed switching frequency. Finally, a comparison between traditional PI control and ST-SMC. The performance of the proposed control, all the simulation tests are in addition carried out with conventional PI control. The obtained results have shown an evident superiority of the super-twisting controllers against the PI one, regarding the current and dc voltage tracking, during the transient and the steady-state regimes. The model has been developed and the control strategy is validated under the environment (MATLAB/SIMULINK).
Detecting faults on PV systems is an essential and important part of monitoring and controlling electrical systems. Providing high-quality power supply requires an efficient diagnostic system capable of handling failures in photovoltaic systems. This paper proposes an experimental diagnosis for Photovoltaic Panel using the DS1104 platform in order to implement the diagnosis model developed in Matlab/Simulink® software. The Diagnosis technique has been achieved using an experimental database of climatic and electrical parameters from a PV panel installed at LGEB Laboratory of University of Biskra, (Algeria). The obtained results show a fast classification and give an accurate fault location using the Fuzzy Logic Classifier (FLC) (the investigated faults are: Different cases of shading effect and short-circuit of the By-pass diode).
