In engineering, a design is best described based on its alternative performance operation. In this paper, an electric power plant is analysed based on its effective operational performance even during critical situation or crisis. Data is generated and analysed using both quantitative and qualitative research approach. During maintenance operation of an electric power plant, some components are susceptible to wide range of issues or crises. These includes natural disasters, supply chain disruptions, cyberattacks, and economic downturns. These crises significantly impact power plant operations and its maintenance strategies. Also, the reliable operation of power plants is often challenged by various technical, operational, and environmental issues. In this research, an investigation is conducted on the problems associated with electric power plants by proposing a comprehensive and novel framework to maintenance the power plant during crises. Based on the achieved results discussed, the framework impact and contribution are the integration of proactive maintenance planning, resilient maintenance strategies, advanced technologies, and adaptive measures to ensure the reliability and resilience of electric power plant during power generation operations in the face of unforeseen challenges/crisis. Hypothetical inferences are used ranging from mechanical failures to environmental constraints. The research also presents a structured approach to ensure continuous operation and effective maintenance in the electric power plant, particularly during crisis (such as environmental issues and COVID-19 pandemic issues).

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

In this paper, a solar tracking device that can continuously track the sun by adjusting the direction and angle of the solar panel in real time is designed and fabricated to improve the power generation efficiency of the solar cell panel. The mechanical parts as well as the automatic control part of the passive sun-tracking system are described, and the efficiency enhancement with the sun-tracking solar panel is characterized in comparison with the fixed panel system. The test results show that in the spring season in Qingdao city of eastern China, the sun-tracking system can improve the solar cell power generation efficiency by 28.5%–42.9% when comparing to the direction and elevation angle fixed system in sunny days. Even in partly cloudy days, the PV power output can increased by 37% with using the passive sun-tracking system. Economic analysis results show the cost-benefit period is about 10 years, which indicates that the passive sun tracking device can substantially contribute to the solar energy harvest practices.

Two kinds of solar thermal power generation systems (trough and tower) are selected as the research objects. The life cycle assessment (LCA) method is used to make a systematic and comprehensive environmental impact assessment on the trough and tower solar thermal power generation. This paper mainly analyzes the three stages of materials, production and transportation of two kinds of solar thermal power generation, calculates the unit energy consumption and environmental impact of the three stages respectively, and compares the analysis results of the two systems. At the same time, Rankine cycle is used to compare the thermal efficiency of the two systems.

In this study, daily averages of air quality parameters were measured in two stations (S1 and S2) of the organized industrial district in Samsun. The meteorological variables were measured at only one station (S1), such as temperature, relative humidity, wind speed, solar radiation, and ambient pressure in 2007, and the daily promised limit for nitrogen dioxide has been especially exceeded at 206 times for 1^st station. However, exceeds of the limit value in 2006 for 1^st station was reduced by approximately 3.5 times. The daily nitrogen dioxide concentration did not exceed the daily limit of WHO^[1] as for 2^nd station. The results obtained showed that under the influence of dominant wind direction, the second station measurement results are higher than that of the first station. To determine all of the possible environmental effects, the measurements should be analyzed from a multi-point perspective.