Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

Evaluation of occupational health management status and safety issues of the small-scale fisheries sector in Bangladesh

Background: Small-scale fishing is one of the most precarious occupations, with high rates of threats and hazards. The present study was undertaken to evaluate the health hazards and safety issues of fishers involved in small-scale fisheries (SSF).Materials and methods: Fifty SSF fishers (n = 50) were surveyed by using a pre-tested questionnaire between October 2019 and March 2020 at the lower Meghna River in the northern tip of the Bay of Bengal, Bangladesh.Results: Results revealed that 56% of SSF fishermen belong to a nuclear family, and 42% completed primary education. Forty per cent had an annual income of between 1,000 and 1,500 USD. Seventy-six per cent of fishermen were found to suffer from fever, and 72%, and 60% from diarrhoea and skin diseases over the last 5 years (2015–2020), respectively. During fishing, 78% of fishermen also suffered from red-eye problems, dizziness, and headache, and 68% struggled with musculoskeletal complaints during the last 5 years. Extreme cyclonic occurrences and sudden storms were experienced by 66% and 32% of fishermen, respectively, during the last 5 years. Local pharmacies were visited by 46% of fishermen for treatment due to ease of access. Sixty-four per cent of participants applied their local indigenous knowledge to treat health-related problems. Twenty-eight per cent and 32% of fishermen used a first aid box and stored medicine on board, respectively.Conclusions: Most of the fishers are in great risk of medium- to high-range danger while fishing in the SSF sector in Bangladesh. Many countries have developed protocols for safe and responsible fishing. In Bangladesh, adequate attention is needed for the sustainable development of the SSF sector