Bayesian reinforcement learning with MCMC to maximize energy output of vertical axis wind turbine

Optimization of energy output of small scale wind turbines requires a controller which keeps the wind speed to rotor tip speed ratio at the optimum value. An analytic solution can be obtained if the dynamic model of the complete system is known and wind speed can be anticipated. However, not only aging but also errors in modeling and wind speed prediction prevent a straightforward solution. This thesis proposes to apply a reinforcement learning approach designed to optimize dynamic systems with continuous state and action spaces, to the energy output optimization of Vertical Axis Wind Turbines (VAWT). The dynamic modeling and load control of the wind turbine are accomplished in the same process. The proposed algorithm is a model-free Bayesian Reinforcement Learning using Markov Chain Monte Carlo method (MCMC) to obtain the parameters of an optimal policy. The proposed method learns wind speed pro les and system model, therefore, can utilize all system states and observed wind speed pro les to calculate an optimal control signal by using a Radial Basis Function Neural Network (RBFNN). The proposed method is validated by performing simulation studies on a permanent magnet synchronous generator-based VAWT Simulink model to compare with the classical Maximum Power Point Tracking (MPPT). The results show signi cant improvement over the classical method, especially during the wind speed transients, promising a superior energy output in turbulent settings; which coincide with the expected application areas of VAWT

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Offshore Wind Farm-Grid Integration: A Review on Infrastructure, Challenges, and Grid Solutions

Recently, the penetration of renewable energy sources (RESs) into electrical power systems is witnessing a large attention due to their inexhaustibility, environmental benefits, storage capabilities, lower maintenance and stronger economy, etc. Among these RESs, offshore wind power plants (OWPP) are ones of the most widespread power plants that have emerged with regard to being competitive with other energy technologies. However, the application of power electronic converters (PECs), offshore transmission lines and large substation transformers result in considerable power quality (PQ) issues in grid connected OWPP. Moreover, due to the installation of filters for each OWPP, some other challenges such as voltage and frequency stability arise. In this regard, various customs power devices along with integration control methodologies have been implemented to deal with stated issues. Furthermore, for a smooth and reliable operation of the system, each country established various grid codes. Although various mitigation schemes and related standards for OWPP are documented separately, a comprehensive review covering these aspects has not yet addressed in the literature. The objective of this study is to compare and relate prior as well as latest developments on PQ and stability challenges and their solutions. Low voltage ride through (LVRT) schemes and associated grid codes prevalent for the interconnection of OWPP based power grid have been deliberated. In addition, various PQ issues and mitigation options such as FACTS based filters, DFIG based adaptive and conventional control algorithms, ESS based methods and LVRT requirements have been summarized and compared. Finally, recommendations and future trends for PQ improvement are highlighted at the end

Wind Power Integration into Power Systems: Stability and Control Aspects

Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting

Towards An Intelligent Fuzzy Based Multimodal Two Stage Speech Enhancement System

This thesis presents a novel two stage multimodal speech enhancement system, making use of both visual and audio information to filter speech, and explores the extension of this system with the use of fuzzy logic to demonstrate proof of concept for an envisaged autonomous, adaptive, and context aware multimodal system. The design of the proposed cognitively inspired framework is scalable, meaning that it is possible for the techniques used in individual parts of the system to be upgraded and there is scope for the initial framework presented here to be expanded. In the proposed system, the concept of single modality two stage filtering is extended to include the visual modality. Noisy speech information received by a microphone array is first pre-processed by visually derived Wiener filtering employing the novel use of the Gaussian Mixture Regression (GMR) technique, making use of associated visual speech information, extracted using a state of the art Semi Adaptive Appearance Models (SAAM) based lip tracking approach. This pre-processed speech is then enhanced further by audio only beamforming using a state of the art Transfer Function Generalised Sidelobe Canceller (TFGSC) approach. This results in a system which is designed to function in challenging noisy speech environments (using speech sentences with different speakers from the GRID corpus and a range of noise recordings), and both objective and subjective test results (employing the widely used Perceptual Evaluation of Speech Quality (PESQ) measure, a composite objective measure, and subjective listening tests), showing that this initial system is capable of delivering very encouraging results with regard to filtering speech mixtures in difficult reverberant speech environments. Some limitations of this initial framework are identified, and the extension of this multimodal system is explored, with the development of a fuzzy logic based framework and a proof of concept demonstration implemented. Results show that this proposed autonomous,adaptive, and context aware multimodal framework is capable of delivering very positive results in difficult noisy speech environments, with cognitively inspired use of audio and visual information, depending on environmental conditions. Finally some concluding remarks are made along with proposals for future work

Temporal Information in Data Science: An Integrated Framework and its Applications

Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems.Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems