Fuzzy logic controller parameter optimization using metaheuristic Cuckoo search algorithm for a magnetic levitation system

The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.Peer ReviewedPostprint (published version

A comparison of fuzzy-based energy management systems adjusted by nature-inspired algorithms

The growing energy demand around the world has increased the usage of renewable energy sources (RES) such as photovoltaic and wind energies. The combination of traditional power systems and RESs has generated diverse problems due especially to the stochastic nature of RESs. Microgrids (MG) arise to address these types of problems and to increase the penetration of RES to the utility network. A microgrid includes an energy management system (EMS) to operate its components and energy sources efficiently. The objectives pursued by the EMS are usually economically related to minimizing the operating costs of the MG or maximizing its income. However, due to new regulations of the network operators, a new objective related to the minimization of power peaks and fluctuations in the power profile exchanged with the utility network has taken great interest in recent years. In this regard, EMSs based on off-line trained fuzzy logic control (FLC) have been proposed as an alternative approach to those based on on-line optimization mixed-integer linear (or nonlinear) programming to reduce computational efforts. However, the procedure to adjust the FLC parameters has been barely addressed. This parameter adjustment is an optimization problem itself that can be formulated in terms of a cost/objective function and is susceptible to being solved by metaheuristic nature-inspired algorithms. In particular, this paper evaluates a methodology for adjusting the FLC parameters of the EMS of a residential microgrid that aims to minimize the power peaks and fluctuations on the power profile exchanged with the utility network through two nature-inspired algorithms, namely particle swarm optimization and differential evolution. The methodology is based on the definition of a cost function to be optimized. Numerical simulations on a specific microgrid example are presented to compare and evaluate the performances of these algorithms, also including a comparison with other ones addressed in previous works such as the Cuckoo search approach. These simulations are further used to extract useful conclusions for the FLC parameters adjustment for off-line-trained EMS based designs.This work is part of the projects 2019-PIC-003-CTE and 2020-EXT-007 from the Research Group of Propagation, Electronic Control, and Networking (PROCONET) of Universidad de las Fuerzas Armadas ESPE. This work has been developed with the support of VLIR-UOS and the Belgian Development Cooperation (DGD) under the project EC2020SIN322A101. This work has been partially supported by the Spanish Ministry of Industry and Competitiveness under the grant DPI2017-85404 and PID2019-111443RB-100.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

An energy management system design using fuzzy logic control: smoothing the grid power profile of a residential electro-thermal microgrid

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work deals with the design of a Fuzzy Logic Control (FLC) based Energy Management System (EMS) for smoothing the grid power profile of a grid-connected electro-thermal microgrid. The case study aims to design an Energy Management System (EMS) to reduce the impact on the grid power when renewable energy sources are incorporated to pre-existing grid-connected household appliances. The scenario considers a residential microgrid comprising photovoltaic and wind generators, flat-plate collectors, electric and thermal loads and electrical and thermal energy storage systems and assumes that neither renewable generation nor the electrical and thermal load demands are controllable. The EMS is built through two low-complexity FLC blocks of only 25 rules each. The first one is in charge of smoothing the power profile exchanged with the grid, whereas the second FLC block drives the power of the Electrical Water Heater (EWH). The EMS uses the forecast of the electrical and thermal power balance between generation and consumption to predict the microgrid behavior, for each 15-minute interval, over the next 12 hours. Simulations results, using real one-year measured data show that the proposed EMS design achieves 11.4% reduction of the maximum power absorbed from the grid and an outstanding reduction of the grid power profile ramp-rates when compared with other state-of-the-art studies.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Photovoltaic power forecast using deep learning techniques with hyperparameters based on bayesian optimization: a case study in the Galapagos Islands

Hydropower systems are the basis of electricity power generation in Ecuador. However, some isolated areas in the Amazon and Galapagos Islands are not connected to the National Interconnected System. Therefore, isolated generation systems based on renewable energy sources (RES) emerge as a solution to increase electricity coverage in these areas. An extraordinary case occurs in the Galapagos Islands due to their biodiversity in flora and fauna, where the primary energy source comes from fossil fuels despite their significant amount of solar resources. Therefore, RES use, especially photovoltaic (PV) and wind power, is essential to cover the required load demand without negatively affecting the islands’ biodiversity. In this regard, the design and installation planning of PV systems require perfect knowledge of the amount of energy available at a given location, where power forecasting plays a fundamental role. Therefore, this paper presents the design and comparison of different deep learning techniques: long-short-term memory (LSTM), LSTM Projected, Bidirectional LSTM, Gated Recurrent Units, Convolutional Neural Networks, and hybrid models to forecast photovoltaic power generation in the Galapagos Islands of Ecuador. The proposed approach uses an optimized hyperparameter-based Bayesian optimization algorithm to reduce the forecast error and training time. The results demonstrate the accurate performance of all the methods by achieving a low-error short-term prediction, an excellent correlation of over 99%, and minimizing the training time.Peer ReviewedPostprint (published version

The Cuckoo search algorithm applied to fuzzy logic control parameter optimization

In the design of control systems, the tuning of controller parameters has a fundamental role in the performance of both transient and steady-state regimes. From this perspective, the tuning of controller parameters has been carried out using perturbation and observation methods, computational tools based on optimization algorithms for low-complexity systems, and more recently, using metaheuristic algorithms for highly complex systems with improved tuning procedures that guarantee the operation and stability of the systems. Thus, avant-garde optimization algorithms that mimic the evolution of self-organizing biological systems, also called metaheuristic nature-inspired algorithms, have gained high relevance due to their great potential for solving optimization problems. Hence, the Cuckoo Search (CS) algorithm, a very promising and nearly recent developed nature-inspired algorithm, has been used in the design and optimization of Fuzzy Logic Control (FLC) systems due to its great potentiality. In particular, this chapter studies the application of the CS algorithm for tuning controller parameters in two different case studies. The first one is associated with the FLC parameter tuning of a nonlinear magnetic levitation system, and the second case study is related to the FLC optimization of the energy management system of a residential microgrid. Simulation results are provided to emphasize and analyze the features of the optimized controllers for the two cases and compared against other more conventional techniques. Obtained outcomes show that the adjustment of FLC parameters, performed through the CS algorithm, is efficient and improves the performance of the two FLC, which makes the CS algorithm becomes a powerful alternative for performing the controller parameter tuning in modern control systems.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Fuzzy control-based energy management system for interconnected residential microgrids using the forecasts of power generation and load demand

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The continuous growth of residential rooftop photovoltaic systems carries with it the problem of the penetration of renewable energy in the utility network, which can lead to its saturation. For this reason, the efficient use of the generated renewable power is of great interest today. This paper presents an energy management system design based on fuzzy logic control to perform the power exchange between two neighboring interconnected residential grid-connected microgrids to reduce the power supply by the mains. Each of the microgrids comprises rooftop photovoltaic generation and energy storage systems. The proposed strategy uses generation and demand forecasts for power-sharing from the microgrid with excess energy to the energy deficit microgrid. Simulation results show the improved performance of the proposed energy management compared to each microgrid's behavior when they do not share power.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

A null space algorithm for mixed finite element approximation of Darcy's equation

SIGLEAvailable from British Library Document Supply Centre-DSC:8715.1804(2001-006) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Short-Term forecasting of photovoltaic power in an isolated area of Ecuador using deep learning techniques

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In Ecuador, electricity generation is mainly covered by renewable energy sources that feed the National Interconnected System (NIS). Its economical price means that the installation of systems based on non-conventional renewable energy sources does not represent a benefit for the user. However, rural communities isolated from the NIS do not have electricity services. Due to this, isolated systems become an effective option to supply electricity to these communities. In this regard, the Galapagos Islands have unique biodiversity in the world. Since they are not connected to the NIS, their primary energy sources are based on biogas obtained from fossil fuels, with their negative consequences despite the great potential of solar resources. Hence the need to use non-conventional renewable energy sources that covers the energy demand and do not affect the biodiversity there. Photovoltaic energy forecasting is an essential step in the installation of photovoltaic systems. Prediction models based on deep learning (DL) techniques can obtain a high degree of accuracy in energy prediction tasks. For this reason, this work presents the development of long short-term memory (LSTM) and gated recurrent unit (GRU) models to predict photovoltaic energy in an isolated area of Ecuador. The results highlight the performance of both methods through the achieved short-term prediction.Peer ReviewedPostprint (published version

Fuzzy logic controller design for battery energy management in a grid connected electro-thermal microgrid

A fuzzy logic controller strategy for battery energy management in a grid connected electro-thermal residential microgrid is presented. The fuzzy control policy manages the power of the microgrid storage elements in order to minimize a set of quality indices involving, among others, the power profile exchanged with the mains. Numerical simulations using real measured data generation and consumption are provided to both validate the control design and to highlight the benefits of including thermal elements in the overall energy management strategy of the system. © 2014 IEEE.Peer Reviewe

Fuzzy logic controller design for battery energy management in a grid connected electro-thermal microgrid

A fuzzy logic controller strategy for battery energy management in a grid connected electro-thermal residential microgrid is presented. The fuzzy control policy manages the power of the microgrid storage elements in order to minimize a set of quality indices involving, among others, the power profile exchanged with the mains. Numerical simulations using real measured data generation and consumption are provided to both validate the control design and to highlight the benefits of including thermal elements in the overall energy management strategy of the system. © 2014 IEEE.Peer Reviewe