Decision Trees and Transient Stability of Electric Power Systems

An inductive inference method for the automatic building of decision trees is investigated. Among its various tasks, the splitting and the stop splitting criteria successively applied to the nodes of a grown tree, are found to play a crucial role on its overall shape and performances. The application of this general method to transient stability is systematically explored. Parameters related to the stop splitting criterion, to the learning set and to the tree classes are thus considered, and their influence on the tree features is scrutinized. Evaluation criteria appropriate to assess accuracy are also compared. Various tradeoffs are further examined, such as complexity vs number of classes, or misclassification rate vs type of misclassification errors. Possible uses of the trees are also envisaged. Computational issues relating to the building and the use of trees are finally discussed

Modelling, Simulation and Fuzzy Self-Tuning Control of D-STATCOM in a Single Machine Infinite Bus Power System

© 2019 Bentham Science Publishers. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.2174/2352096511666180314141205In recent years, demand for electricity has increased considerably, while the expansion of generation and transmission has been very slow due to limited investment in resources and environmental restrictions. Methods: As a result, the power system becomes vulnerable to disturbances and instability. FACTS (Flexible AC Transmission Systems) technology has now been accepted as a potential solution to this problem. This paper deals with the modelling, simulation and fuzzy self-tuning control of a D-STATCOM to enhance the stability and improve the critical fault clearing time(CCT) in a single machine infinite bus (SMIB).A detailed modelling of the D-STATCOM and comprehensive derivation of the fuzzy logic self-tuning control is presented. Results: The dynamic performance of the power system with the proposed control scheme is validated through in a simulation study carried out under Matlab/Simulink and SimPowerSystems toolbox. Conclusion: The results demonstrate a significant enhancement of the power system stability under the simulated fault conditions considered.Peer reviewe

Analysis and robust decentralized control of power systems using FACTS devices

Today\u27s changing electric power systems create a growing need for flexible, reliable, fast responding, and accurate answers to questions of analysis, simulation, and design in the fields of electric power generation, transmission, distribution, and consumption. The Flexible Alternating Current Transmission Systems (FACTS) technology program utilizes power electronics components to replace conventional mechanical elements yielding increased flexibility in controlling the electric power system. Benefits include decreased response times and improved overall dynamic system behavior. FACTS devices allow the design of new control strategies, e.g., independent control of active and reactive power flows, which were not realizable a decade ago. However, FACTS components also create uncertainties. Besides the choice of the FACTS devices available, decisions concerning the location, rating, and operating scheme must be made. All of them require reliable numerical tools with appropriate stability, accuracy, and validity of results. This dissertation develops methods to model and control electric power systems including FACTS devices on the transmission level as well as the application of the software tools created to simulate, analyze, and improve the transient stability of electric power systems.;The Power Analysis Toolbox (PAT) developed is embedded in the MATLAB/Simulink environment. The toolbox provides numerous models for the different components of a power system and utilizes an advanced data structure that not only increases data organization and transparency but also simplifies the efforts necessary to incorporate new elements. The functions provided facilitate the computation of steady-state solutions and perform steady-state voltage stability analysis, nonlinear dynamic studies, as well as linearization around a chosen operating point.;Applying intelligent control design in the form of a fuzzy power system damping scheme applied to the Unified Power Flow Controller (UPFC) is proposed. Supplementary damping signals are generated based on local active power flow measurements guaranteeing feasibility. The effectiveness of this controller for longitudinal power systems under dynamic conditions is shown using a Two Area - Four Machine system. When large disturbances are applied, simulation results show that this design can enhance power system operation and damping characteristics. Investigations of meshed power systems such as the New England - New York power system are performed to gain further insight into adverse controller effects

Power management and control strategies for off-grid hybrid power systems with renewable energies and storage

This document is the Accepted Manuscript of the following article: Belkacem Belabbas, Tayeb Allaoui, Mohamed Tadjine, and Mouloud Denai, 'Power management and control strategies for off-grid hybrid power systems with renewable energies and storage', Energy Systems, September 2017. Under embargo. Embargo end date: 19 September 2018. The final publication is available at Springer via https://doi.org/10.1007/s12667-017-0251-y.This paper presents a simulation study of standalone hybrid Distributed Generation Systems (DGS) with Battery Energy Storage System (BESS). The DGS consists of Photovoltaic (PV) panels as Renewable Power Source (RPS), a Diesel Generator (DG) for power buck-up and a BESS to accommodate the surplus of energy, which may be employed in times of poor PV generation. While off-grid DGS represent an efficient and cost-effective energy supply solution particularly to rural and remote areas, fluctuations in voltage and frequency due to load variations, weather conditions (temperature, irradiation) and transmission line short-circuits are major challenges. The paper suggests a hierarchical Power Management (PM) and controller structure to improve the reliability and efficiency of the hybrid DGS. The first layer of the overall control scheme includes a Fuzzy Logic Controller (FLC) to adjust the voltage and frequency at the Point of Common Coupling (PCC) and a Clamping Bridge Circuit (CBC) which regulates the DC bus voltage. A maximum power point tracking (MPPT) controller based on FLC is designed to extract the optimum power from the PV. The second control layer coordinates among PV, DG and BESS to ensure reliable and efficient power supply to the load. MATLAB Simulink is used to implement the overall model of the off-grid DGS and to test the performance of the proposed control scheme which is evaluated in a series of simulations scenarios. The results demonstrated the good performance of the proposed control scheme and effective coordination between the DGS for all the simulation scenarios considered.Peer reviewedFinal Accepted Versio

Analysis of DC microgrids as stochastic hybrid systems

A modeling framework for dc microgrids and distribution systems based on the dual active bridge (DAB) topology is presented. The purpose of this framework is to accurately characterize dynamic behavior of multi-converter systems as a function of exogenous load and source inputs. The base model is derived for deterministic inputs and then extended for the case of stochastic load behavior. At the core of the modeling framework is a large-signal DAB model that accurately describes the dynamics of both ac and dc state variables. This model addresses limitations of existing DAB converter models, which are not suitable for system-level analysis due to inaccuracy and poor upward scalability. The converter model acts as a fundamental building block in a general procedure for constructing models of multi-converter systems. System-level model construction is only possible due to structural properties of the converter model that mitigate prohibitive increases in size and complexity. To characterize the impact of randomness in practical loads, stochastic load descriptions are included in the deterministic dynamic model. The combined behavior of distributed loads is represented by a continuous-time stochastic process. Models that govern this load process are generated using a new modeling procedure, which builds incrementally from individual device-level representations. To merge the stochastic load process and deterministic dynamic models, the microgrid is modeled as a stochastic hybrid system. The stochastic hybrid model predicts the evolution of moments of dynamic state variables as a function of load model parameters. Moments of dynamic states provide useful approximations of typical system operating conditions over time. Applications of the deterministic models include system stability analysis and computationally efficient time-domain simulation. The stochastic hybrid models provide a framework for performance assessment and optimization --Abstract, page iv

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

Real-Time Event-Driven Load Shedding for Power System Transient Stability Control using Deep Learning Techniques

With the increasing integration of variational renewable energy and the more active demand side responses, there are more challenges in maintaining secure and reliable power system operation due to the escalated stochasticity and variations in the system. This can be experienced from recent rolling blackouts over the world. Event-driven load shedding (ELS) serves as a fast and effective stability control scheme for power system after a risky disturbance occurs, which can suppress grid oscillation, recover system stability, and prevent cascading failure. Unlike the response-driven control schemes, ELS executes the load shedding action immediately following the disturbance, which aims to control power system stability at an earlier stage with the minimum amount of control cost. The digitalized power systems deploy advanced measurement devices such as phasor measurement units and smart meters, which provides the adequate sensing infrastructure to implement real-time stability assessment and control. However, the conventional approaches for ELS rely on numerical simulations and iterative optimizations which are computationally burdensome and thus slow reactive to the real-time system variations. More recently, artificial intelligence (AI) techniques provide a new way to realize real-time ELS owing to their fast decision making capability. This research identifies the key issues in existing AI based ELS approaches and proposes a series of novel methodologies based on deep learning techniques to enhance overall ELS performance in practical situations. A deep neural network (DNN) model is first presented to improve the decision-making accuracy on ELS strategy. Moreover, considering the unbalanced control cost induced by an over- and under-estimated ELS amount, a risk-averse learning method for DNN is proposed to increase the likelihood of control success with negligible impairment on control cost. On top of those, a GraphSAGE-based ELS model is proposed to capture and embed the topological structure of power system into deep learning, which further improves the overall control performance of ELS. The proposed methodologies have been tested on New England 39 bus system and Nordic power system. The proposed deep learning methods have shown more exceptional control performance of ELS as compared to the existing methods