The aim of this study was to propose and evaluate a very small wind turbine (VSWT) that competes with commercial grid-connected VSWTs in terms of simplicity, robustness and price. Its main components are a squirrel-cage induction generator (SCIG) driven by a frequency converter. The system has a direct-drive shaft, and may be constructed with commercial equipment. Simulation of the wind turbine effect is done with a motor. A control program regulates the variable-speed of rotation through three operational modes: (i) to drive the turbine to its optimum operation point; (ii) to limit its maximum rotational speed; and (iii) to limit the maximum power it generates. Two tests were performed, in order to evaluate the dynamic response of this system under variable wind speeds. The tests demonstrate that the system operates at the optimum operational point of the turbine, and within the set limits of maximum rotational speed and maximum generated power. The drop in performance in relation to its nominal value is about 75%, when operating at 50% of the nominal power. In summary, this VSWT with its proposed control program is feasible and reliable for operating direct-shaft grid-connected VSWTs.

En este trabajo se presenta un equipo didáctico desarrollado en la Universidad de Burgos en España, utilizado para impartir docencia de laboratorio en las asignaturas de automatización e informática industrial, con la metodología de aprendizaje colaborativo. El equipo está diseñado usando una arquitectura abierta y flexible, tomando en cuenta el ambiente industrial real y la posibilidad de trabajar en grupos, cumpliendo cada alumno diferentes roles. Se muestra que el uso de este equipo en aprendizaje activo aumenta la motivación de los estudiantes y mejora los resultados, con más estudiantes que logran los objetivos del curso. Además, a través de una encuesta se conoce la satisfacción del alumno con la metodología utilizada.

The increasing digitalization of industrial environments, driven by Industry 4.0, has fostered the integration of Cyber-Physical Systems (CPS), which combine Information Technology (IT) and Operational Technology (OT) to enhance efficiency, automation, and real-time decision-making. However, this convergence has also increased the exposure of Industrial Control Systems (ICS) to cyber threats, highlighting the need for tools and frameworks capable of detecting anomalies and cyberattacks. In this context, the present work proposes a testbed for evaluating Artificial Intelligence techniques aimed at the automatic detection of anomalies in industrial processes. The testbed enables the simulation of both normal and anomalous conditions following the GEMMA methodology. A use case is presented in which a supervised machine learning approach—specifically, the K-Nearest Neighbours (KNN), Random Forest (RF), and Support Vector Machine (SVM) algorithms—is applied to data generated in the Testbed for Anomaly Detection and Protection of Industrial Cyber-Physical Systems (DAyPSCI). The results demonstrate the feasibility of the proposed approach for classifying instances based on their operational behaviour, achieving high accuracy in identifying normal patterns and actuator failures, and acceptable performance in detecting sensor anomalies. These findings validate the testbed as a realistic and effective environment for the development and evaluation of intelligent anomaly-detection systems in industrial CPS. Moreover, the proposed testbed can be considered a solid foundation for future research in predictive maintenance, process optimization, and industrial cybersecurity. Keywords: Testbed, Anomaly detection, Cybersecurity, Digital Twin, Cyber-physical system, Programmable Logic Controller (PLC), Machine Learning, Industrial control.

Los Sistemas Ciberfísicos (CPS) industriales, impulsados por la Industria 4.0, han mejorado la monitorización y el control en tiempo real de procesos productivos, así como la automatización en fábricas inteligentes, lo que ha incrementado la demanda de datos. Esta situación ha acelerado la convergencia entre tecnologías de la información (IT) y operativas (OT), aumentando la exposición de sistemas de control, especialmente los basados en Controladores Lógicos Programables (PLCs), a ciberataques. En este contexto este artículo presenta un Banco de Pruebas para la Detección de Anomalías y Protección de Sistemas Ciberfísicos Industriales (DAyPSCI), una plataforma híbrida, abierta y flexible, orientada a la investigación y formación en ciberseguridad aplicada a Sistemas de Control Industrial (ICS). DAyPSCI permite generar conjuntos de datos (datasets) con información de proceso y del estado del sistema, siguiendo la metodología GEMMA. Además, posibilita la ejecución de ataques controlados sobre dispositivos industriales reales, facilitando el estudio de vulnerabilidades y la validación de mecanismos de detección y protección.

This study presents a practical methodology for executing Man-in-the-Middle (MitM) attacks on industrial control systems that utilize PROFINET I/O—a communication layer that remains largely underexplored in ICS cybersecurity research. A hybrid digital-twin-based testbed is developed by integrating Siemens S7-1500 and S7-1200 PLCs with a process replica implemented in PCSimu, together with a malicious application that modifies specific process data before it is delivered through the PROFINET I/O channel, enabling controlled falsification of process information in real time. The attacker operates through a Modbus TCP control channel while injecting the manipulated values into the 40-byte Real-Time Class 1 (RTC1) cyclic process-data payload while preserving frame integrity and protocol-level validity indicators. Experimental results show that SDU-level modifications on the 2-ms RTC1 cycle produced deterministic and fully reproducible effects on PLC-level behavior, including forced actuator confirmations and falsified process states, demonstrating the feasibility of both DI- and DO-level manipulation scenarios. Network captures and MSSQL-based event logs provide bit-level correlation between the injected SDU modifications and their impact on the automation sequence, confirming the reliability of the proposed manipulation mechanism. The testbed also supports the systematic generation of labeled datasets for training and evaluating machine-learning-based intrusion and anomaly-detection methods, and offers direct applicability to research, education, and operator-training activities in industrial cybersecurity. Overall, the proposed platform offers a secure, reproducible, and practically applicable environment for vulnerability assessment, attack simulation, and the development of detection techniques in industrial PROFINET networks.