Biblio

Power system security assessment and enhancement in grids with high penetration of renewables is critical for pragmatic power system planning. Static Security Assessment (SSA) is a fast response tool to assess system stability margins following considerable contingencies assuming post fault system reaches a steady state. This paper presents a contingency ranking methodology using static security indices to rank credible contingencies considering severity. A Modified IEEE 9 bus system integrating renewables was used to test the approach. The static security indices used independently provides accurate results in identifying severe contingencies but further assessment is needed to provide an accurate picture of static security assessment in an increased time frame of the steady state. The indices driven for static security assessment could accurately capture and rank contingencies with renewable sources but due to intermittency of the renewable source various contingency ranking lists are generated. This implies that using indices in future grids without consideration on intermittent nature of renewables will make it difficult for the grid operator to identify severe contingencies and assist the power system operator to make operational decisions. This makes it necessary to integrate the behaviour of renewables in security indices for practical application in real time security assessment.

The Covid-19 Pandemic has caused huge losses worldwide and is still affecting people all around the world. Even after rigorous, incessant and dedicated efforts from people all around the world, it keeps mutating and spreading at an alarming rate. In times such as these, it is extremely important to take proper precautionary measures to stay safe and help to contain the spread of the virus. In this paper, we propose an innovative design of one such commonly used public disinfection method, an Automatic Walkthrough Sanitization Tunnel. It is a walkthrough sanitization tunnel which uses sensors to detect the target and automatically disinfects it followed by irradiation using UV-C rays for extra protection. There is a proposition to add an IoT based Temperature sensor and data relay module used to detect the temperature of any person entering the tunnel and in case of any anomaly, contact nearby covid wards to facilitate rapid treatment.

The monitoring circuit is widely applied in radiation environment and it is of significance to study the circuit reliability with the radiation effects. In this paper, an intelligent analysis method based on Deep Belief Network (DBN) and Support Vector Method is proposed according to the radiation experiments analysis of the monitoring circuit. The Total Ionizing Dose (TID) of the monitoring circuit is used to identify the circuit degradation trend. Firstly, the output waveforms of the monitoring circuit are obtained by radiating with the different TID. Subsequently, the Deep Belief Network Model is trained to extract the features of the circuit signal. Finally, the Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied to classify and predict the remaining useful life (RUL) of the monitoring circuit. According to the experimental results, the performance of DBN-SVM exceeds DBN method for feature extraction and classification, and SVR is effective for predicting the degradation.