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Power plants become critical facilities that need to run every time without failure. The component in the power plant system has been equipped with sensors. These sensors will monitor the element's condition by periodically sending the related metric data to the control room or storing it for later evaluation. These sensor data can be analyzed for early anomaly detection on power plants. Traditional methods still need humans as experts to detect these anomalies; hence, human error is a factor. Using deep learning, we can take advantage of abundant data to learn the usual pattern and detect anomalies in future data. Many deep learning algorithms have been implemented in this case. However, the selection of these algorithms depends on the data's characteristics. This paper aims to provide a comprehensive, structured analysis of the data features and the algorithm used to detect anomalies in the data. Twenty-two recent power plant anomaly detection studies were compiled as a systematic review. This review will discuss power plant sensor data characteristics that need to be considered, such as raw data format, label usage, data dependence, and detection results. This review also discusses methods used to process unsupervised data, the relation of the multivariate data, and anomaly detection. This review aims to ease the selection of the appropriate anomaly detection algorithm for future research by considering the data characteristics. [ABSTRACT FROM AUTHOR]

In order to meet the need for cleaner energy in the Green House Gas (GHG) emission reduction framework, the implementation of biomass cofiring in the coal-fired power plant (CFPP) is important. Cofiring of biomass and coal in CFPP is considered an alternative solution to answer the GHG emission problems and extend the life of power plants. Due to different characteristics of biomass compared to coal, however, the implementation of biomass cofiring in CFPP has faced many technical obstacles. It can increase the risk of slagging-fouling and corrosion due to the mineral ash and chlorine contents. This paper presents experimental investigations on the cofiring of palm oil waste with pulverized coal using a drop tube furnace. The characteristics of the composition of the ash particles were analyzed, Ash Fusion temperature (AFT), probe observation from DTF, also analyzed using scanning electron microscopy with energy dispersive X-ray (SEM-EDX). The experiment results show that cofiring palm oil waste with coal during combustion affects ash due to various physical and chemical properties. Cofiring coal with 25% of the biomass, either with an empty fruit bunch or oil palm frond, increases the slagging observed from the probe's deposits. The addition of 25% biomass, a blend of EFB and frond with a composition of 50% each, is better from the slagging aspect than single biomass. Further research is required to investigate the effect of different biomass proportions by weight, fouling's risk, and additives' use to mitigate slagging risk. [ABSTRACT FROM AUTHOR]