Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

This paper presents the strategies and initial implementations of a data culture conversion process at a company in the electricity sector. Solutions are being developed within the scope of the construction management macro-process, which involves multiple sectors of the company, as well as external agents. The strategy is based on implementing Proofs of Concept as tools for converting the data culture within the company and, at the same time, supporting the essential activities of construction management. The proofs of concept aligned with the change in data culture have two components: technology and people. The first consists mainly of intelligent automation and process optimization. The second component involves sensitization, training and developing solutions together with the company's employees. The results involve a reduction in workload since some activities have been partially or totally transferred to the software. Greater assertiveness in estimating numerical data, such as prices and budgets. Increased reliability by reducing human intervention and complex calculations. Greater controllability by monitoring multiple activities and sectors, simplifying management. Finally, the integration of technology and people has shown significant advances in organizational culture, enabling employees to have a greater understanding of advanced tools and methods and to be able to identify opportunities and share solutions within the company.

Due to the great rise of robots globally, such equipment has become fundamental for achieving a manufacturing plant's daily productivity and quality goals. A failure or anomaly that may occur with a machine can lead to a major stoppage of the machine or even the production line. In this context, the study of prior detection of anomalies in robots becomes suitable for planned preventive maintenance, production continuity, and equipment longevity, which is the main objective of this work. The methodology comprises constructing a dataset with positioning anomalies generated by a robot on a real production line. The Isolation Forest model was selected to operate as an anomaly detector. The model achieved a macro F1-score equal to 0.987, which is suitable for the monitoring in question, given that the model was developed in a real assembly line. This model was also implemented in the machine's controller with an execution response time of 565 ms, without compromising the controller's other tasks and allowing continuous assessment of robot failures—configuring the solution as an edge device. Therefore, this work extends the literature by demonstrating the detection of anomalies in a real production scenario, a subject underexplored in recent works.

The main objective of this work is to present a methodology and software tools that supports the efficient management of transmission line projects through integrated reports from various data sources and systems. This enables the management team to monitor project performance and make informed decisions to maximize overall returns for the company. To achieve this, the research adopts a business intelligence and data analysis-based approach, which includes mapping and organizing information from internal systems, creating a standardized data structure, and implementing managerial reports on a BI platform to monitor resource allocation and efficiency. As outcomes, the study presents a set of dashboards and reports offering an integrated and comprehensive view of activities, including physical and financial progress indicators, risk analysis, and optimization in human resource allocation, highlighting significant savings in project budgets. This approach (methodology and tools) facilitates more accurate and timely analysis, enhances decision-making, efficiency and reliability, and provides strategic advantages for concessionaires in terms of competitiveness, operational sustainability and greater control over the resources employed in transmission projects.