Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
Browse
Search Results
- A rule-based selection algorithm for enhancing power quality in electrical distribution systems with microgrid controllers(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-08) Valencia Rivera, Gerardo Humberto; Amaya Contreras, Iván Mauricio; emipsanchez; Soares Moura, Pedro Manuel; Castañeda Cuevas, Hernán; Juárez Jiménez, Julio Antonio; School of Engineering and Sciences; Campus Monterrey; Aviña Cervantes, Juan GabrielThe increasing integration of microgrids into electrical distribution systems presents opportunities and challenges in maintaining power quality (PQ) and grid stability, which are essential to ensure reliable operation, prevent equipment damage, and avoid service interruptions. Conventional control strategies often struggle to adapt to fluctuating PQ disturbances due to their limited flexibility and reliance on static parameters, which make them less responsive to dynamic and unpredictable grid conditions. This constrains their ability to mitigate issues such as current imbalances, where the current is unevenly distributed across the phases of the electrical system, and harmonic distortion, where current waveforms deviate from their ideal sinusoidal shape. This dissertation proposes a selection framework inspired by rule-based hyper-heuristics, justified by their flexibility and ability to generalize decision strategies across a wide range of PQ scenarios, making them suitable for dynamic and unpredictable environments. The framework regulates microgrid control actions by assigning optimized weights to a set of decision rules, enabling adaptive responses to fluctuating PQ disturbances. The selection scheme profits metaheuristic optimization techniques, specifically, the grey wolf optimizer, micro-Genetic algorithm, and the inertial version of particle swarm optimization to tune a rule-based decision-making mechanism. These metaheuristics were selected because of their reported effectiveness in optimizing power systems and industrial applications. The selection framework was evaluated using a single microgrid connected to a simplified low-voltage distribution model with residential loads operating at 120/240 V and 60 Hz, representing North American systems and subjected to close-to-reality PQ disturbances. The proposed framework was also rigorously evaluated across 90 PQ scenarios, demonstrating superior adaptability compared to standalone controllers. This adaptability is reflected in the model’s ability to maintain 93.33% of instances within the PQ thresholds ruled by international standards. Additionally, the rule-driven approach reduced around 80% of power losses provoked by current imbalances and harmonic distortions on average, translating to annual energy savings of up to 25,000 kWh (4,000 USD), reinforcing its economic and operational advantages. The findings of this dissertation suggest three potential research paths: scaling up to larger distribution systems by addressing computational complexity concerns, generalizing to diverse grid scenarios, and developing hybrid artificial intelligence approaches to refine decision-making processes. Therefore, this research contributes to the intersection of electrical engineering, control systems, and computer science by demonstrating how existing metaheuristic techniques can be integrated into a rule-based selection framework for decision-making under real-world scenarios. The computational contribution lies in the design, tuning, and validation of a cooperative control scheme that generalizes across diverse operating conditions, serving as a practical proof of concept for applying heuristic-driven decision models to dynamic energy systems.
- Passive decentralized island mode detection and optimization-based design of passive filters for disconnection events in microgrid systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-06) López Gutiérrez, Juan Roberto; Ponce Cruz, Pedro; puemcuervo; Balderas Silva, David Christopher; Reyes Rosario, Alfredo; Soriano Avedaño, Luis Arturo; School of Engineering and Sciences; Campus Ciudad de México; Ibarra Moyers, Luis MiguelIn recent years, the electrical network has been evolving towards becoming a more sustainable system, the present environmental concerns regarding the greenhouse gas emission by the energy sector have pushed forward the integration of alternative generation units that promote the decarbonization of the energy production sector. Over the past decades the integration of these ``cleaner'' energy generation systems has been done in an On-grid and an Off-grid fashion, however, this integration strategy encountered some problems regarding key areas such as control and management, just to mention a few. The microgrid concept is then created to overcome these issues, allowing a seamless integration to the electrical network of the growing alternative generation assets, improving how these ``cleaner'' energy production alternatives are managed into more sustainable systems. In Microgrids with a high penetration of renewable energy sources, power converters are used to regulate the produced energy to a single voltage and frequency reference value across the microgrid. Adequate incorporation of an LC filter at the output of power electronic devices allows the attenuation of harmful harmonics that can be introduced to the microgrid's energy bus. By traditional methods, LC filter values can be calculated by means of the power rating, switching frequency, cutoff frequency, and using the bode frequency domain. It is important to consider that, a microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation. The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can be harmful to the grid, and compensating actions must be triggered to avoid service interruption, preserve power quality, and minimize the possibility of faults. It is important to consider that in transition from a connected to an autonomous microgrid operation, the calculated LC filter can lead to high harmonic injection. As a result, a tuning methodology capable of obtaining the right set of parameters for the LC filter for such transition events can improve the performance of the microgrid. Alternately, such transition events must be detected to enable compensating action; island detection methods are essential to this end. Such techniques typically depend on communication networks or on the introduction of minor electrical disturbances to identify and broadcast unexpected islanding events. However, local energy resources are distributed, variable, and are expected to be integrated in a plug-and-play manner; then, conventional island detection strategies can be ineffective as they rely on specific infrastructure. To overcome those problems, this work proposes to improve the islanding phenomenon in two main contributions. To tackle the issues in regards to the introduction of harmful transients by traditional LC filters, this work optimizes the LC output parameters with respect to the size of the filter components, the IEEE Std 519-2014, and bandwidth of the filter, within a bounded region of values subjected to performance conditions such as voltage output, and the produced total harmonic distortion measurements during the transition from a connected to an autonomous operation. In a case study, genetic algorithm optimization is used to obtain the LC filter parameters and compared to a conventional arithmetic methodology to obtain the values of the filter. The optimization results in a set of values that lead to a higher harmonic attenuation after the transition rather than a conventional method using the switching frequency as the main design factor. In the other end of the islanding phenomenon, where islanding events must be detected while avoiding traditional infrastructure setbacks, a straightforward, distributed island detection technique is proposed, this technique relies only on local electrical measurements, available at the output of each generating unit. The proposed method is based on the estimated power-frequency ratio, associated with the stiffness of the grid. A ``stiffness change'' effectively reveals island operating conditions, discards heavy load variations, and enables independent (distributed) operation. The proposal was validated through digital simulations and an experimental test-bed. Such test-bed consists of a Real-Time HIL implementation, the proposed island detection algorithm is programmed to run in an embedded format while connected to a Real-Time simulator running a microgrid equivalent model in the form of a three-phase parallel RLC load as recommended by the IEEE Std. 929 and IEEE Std. 1547 for islanding detection. Results showed that the proposed technique can effectively detect island operation at each generating unit interacting in the microgrid. Moreover, it was about three times faster than other reported techniques.