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Earthquakes are a significant risk contributor of nuclear power plants, potentially causing simultaneous damage to multiple structures, systems, and components. Various approaches have been proposed to estimate the probability of such simultaneous failures, among which the Reed-McCann procedure is widely recognized and recommended. However, its limitations and differences compared to alternative approaches remain insufficiently explored. In this study, we conduct an analytical comparison between the Reed-McCann procedure and the multivariate normal model, another well-established method in seismic probabilistic risk assessment, to identify a more practical method. To facilitate this comparison, we extend the Reed-McCann procedure by introducing negative common variables, which enables the procedure to represent the parameter space of the multivariate normal model. Then, under the assumptions of linear response and the separation of variables, we prove that the extended Reed-McCann procedure can be expressed as the multivariate normal model. Furthermore, we derive a transformation formula to convert the parameters of the Reed-McCann and extended Reed-McCann procedures into those of the multivariate normal model. This equivalence establishes a common foundation for an analytical comparison to identify a practical method. Our analysis demonstrates that the multivariate normal model offers superior computational efficiency, owing to its lower number of integration variables and an available fast evaluation algorithm. Moreover, the parameter estimation in the multivariate normal model is more straightforward than the Reed-McCann and extended Reed-McCann procedures. These advantages support our recommendation of the multivariate normal model as a preferable choice for practical estimation of seismically induced joint failure probability.
Integration of renewable energy, at a large system level, is providing challenges to the power grid due to its intermittent nature causing peak loads and potential grid instability. Demand response can serve to mitigate these effects by matching flexible loads and supply of renewables. But demand response is operational, only when accurate and easy load monitoring method are available. One of the load monitoring approaches is NILM that infers appliance-level usage patterns from aggregate (smart meter) data. This article introduces a MILP based NILM formulation. The aim trades off between reconstruction error and sparsity, switching, smoothness and baseline penalties. Simulation results on synthetic household data indicate that the proposed technique results in very precise appliance recovery at a small computational cost, therefore indicating that optimization-based NILM becomes viable when nominal appliance ratings are available.
The rise in Electric Vehicle (EV) usage has significantly increased the need for high-power fast charging systems that must meet rigorous performance standards. This paper examines the performance of 50 kW grid-connected EV charger. The design of the charger utilizes a dual stage Vienna rectifier at the front end, combined with a bi-directional DC-DC stage, to achieve a high-power factor, minimize total harmonic distortion (THD), and maintain stable operation of the DC bus. Vienna rectifiers are commonly utilized in high-power electric vehicle chargers because of their excellent efficiency greater than 94% and nearly unity power factor. An analysis of 50 kW charger that employs a Vienna rectifier focuses on its output ripple, input signal distortion, and power input factor. Under standard operating conditions, the performance remains equable. However, once the battery charge status surpasses 80%, the notable decline in performance occurs. In this scenario, both ripple and THD increase, and the power factor strays from unity, potentially harming the battery State of Health (SOH) during constant-voltage charging. Based on research results, this paper quantified the implication of current ripple on conversion efficiency in Electric vehicle charger through experimental verification and the results communicate that the current ripples have important influence on EV chargers.
Coal is the most extensively used fuel for thermal power generation, but burning fossil fuels is a major contributor to air pollution due to carbon dioxide emissions. Biomass has a significant potential energy source and can be blended with coal to reduce emissions of greenhouse gases for example CO2, NOx and SOx. Unlike fossil fuels, burning biomass does not increase the quantity of carbon dioxide in the atmosphere because the carbon consumed for growth is offset. The combination of biomass and coal can also have environmental benefits by helping to limit climate change. This study analysed the combustion of coal (C) and rice husk (RH) blends at different weight percentages (5
The objective of this study is to realize short-term forecasting of time-varying solar irradiation and wind speed through a unified neural-network-based algorithm. In previous work, a neural-network forecasting model for solar irradiation—benchmarked against the Smart Persistence Model based on the Clear Sky Index (CSI)—demonstrated stable and accurate performance. In the present study, this framework was extended to wind speed forecasting by introducing a new indicator, the Average Index (AI), which plays an analogous role to CSI. Solar forecasting was applied to the Kyushu region, while wind forecasting was conducted for Okinawa and Iwate Prefectures, where high wind-power potential exists. Using meteorological data from the Japan Meteorological Agency, the proposed method achieved high accuracy and demonstrated strong agreement with the Smart Persistence Model. The integration of AI into the neural-network system enabled reliable short-term forecasting of wind speed, confirming that solar irradiation and wind speed can both be predicted within a single, unified algorithm. This framework provides a robust foundation for multi-source renewable-energy forecasting and intelligent grid management.
