Evolutionary model type selection for global surrogate modeling

Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type

Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Enhancement of Power System Dynamic Performance by Coordinated Design of PSS and FACTS Damping Controllers

Due to environmental and economical constraints, it is difficult to build new power lines and to reinforce the existing ones. The continued growth in demand for electric power must therefore to a great extent be met by increased loading of available lines. A consequence of this is reduction of power system damping, leading to a risk of poorly damped power oscillations between generators. To suppress these oscillations and maintain power system dynamic performance, one of the conventional, economical and effective solutions is to install a power system stabilizer (PSS). However, in some cases PSS may not provide sufficient damping for the inter-area oscillations in a multi-machine power system. In this context, other possible solutions are needed to be exposed. With the evolution of power electronics, flexible AC transmission systems (FACTS) controllers turn out to be possible solution to alleviate such critical situations by controlling the power flow over the AC transmission line and improving power oscillations damping. However, coordination of conventional PSS with FACTS controllers in aiding of power system oscillations damping is still an open problem. Therefore, it is essential to study the coordinated design of PSS with FACTS controllers in a multi-machine power system. This thesis gives an overview of the modelling and operation of power system with conventional PSS. It gives the introduction to emerging FACTS controllers with emphasis on the TCSC, SVC and STATCOM controllers. The basic modelling and operating principles of the controllers are explained in this thesis, along with the power oscillations damping (POD) stabilizers. The coordination design of PSS and FACTS damping controllers over a wide range of operating conditions is formulated as an optimization problem. The objective function of this optimization problem is framed using system eigen values and it is solved using AAPSO and IWO algorithms. The optimal control parameters of coordinated controllers are obtained at the end of these optimization algorithms. A comprehensive approach to the hybrid coordinated design of PSS with series and shunt FACTS damping controllers is proposed to enhance the overall system dynamic performance. The robustness and effectiveness of proposed hybrid coordinated designs are demonstrated through the eigen value analysis and time-domain simulations. The proposed hybrid designs provide robust dynamic performance under wide range in load condition and providing significant improvement in damping power system oscillations under severe disturbance. The developed hybrid coordinated designs are tested in different multimachine power systems using AAPSO and IWO algorithms. The IWO based hybrid designs and AAPSO based hybrid designs are more effective than other control designs. In addition to this, the proposed designs are implemented and validated in real-time using Opal-RT hardware simulator. The real-time simulations of different test power systems with different proposed designs are carried out for a severe fault disturbance. Finally, the proposed controller simulation results are validated with real-time results

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio

Freeway Performance Measurement in a Connected Vehicle Environment Utilizing Traffic Disturbance Metrics

The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic measures that can be used in combination with macroscopic measures for better estimation of traffic safety and mobility. This dissertation examines the use of microscopic measures in combination with the usually used macroscopic measures for traffic congestion evaluation, traffic state categorization, traffic flow breakdown prediction, and estimation of traffic safety. The considered macroscopic measures are the mean speed, traffic flow rate, and occupancy. The investigated microscopic measures for the stated purpose are: standard deviations of individual vehicle’s speeds, standard deviation of vehicles’ speed, and disturbance metrics. The utilized disturbance metrics to capture the stop-and-go operations are: the number of oscillations and a measure of disturbance durations in terms of the time exposed time–to–collision (TET), which has been used in other studies as a safety surrogate measure. However, this measure of disturbance duration requires the location and speed of both the leading and following vehicles and therefore cannot be measured accurately with low sample sizes of connected vehicles (CV). Thus, this study derived a model to estimate this measure based on speed parameters. The developed model was tested using real-world trajectory data from two locations that were not used in the development of the model. Moreover, the percentage of vehicles in the platoon and the platoon size distribution were evaluated as additional indicators of congestion. The relationship between the platooning and disturbance metrics and the speed parameters were further explored. It is recognized that the parameters required to identify the platoons, such as the time headway, will not be available based on data from low market penetrations of CV. Thus, a model was developed that utilize other measures for the estimation of the platooning measures at lower CV market penetrations. For the purpose of traffic state recognition and prediction, first, the study used a hybrid of two unsupervised clustering techniques to classify traffic states into “breakdown” and “non-breakdown”. The study found that adding the disturbance metrics in data clustering when identifying the traffic states will result in better traffic state recognition and traffic flow breakdown identification by capturing the disturbances in the traffic stream. The categorized traffic state was then used as a binary response to the macroscopic and microscopic measures, as features, to train supervised machine learning techniques for predicting traffic flow breakdown in the following 5-minute interval in real-time operations. The study found that utilizing disturbance and safety surrogate metrics in the real-time classification of traffic flow state increases the accuracy of prediction. Also, the study showed that the investigated disturbance metrics and associated models and thresholds are significantly related to crash frequencies and thus can be used in the activation of transportation management strategies to reduce the probability of unsafe traffic and ease traffic disturbances that have adverse impact on traffic safety

Power System Dynamic Control and Performance Improvement Based on Reinforcement Learning

This dissertation investigates the feasibility and effectiveness of using Reinforcement Learning (RL) techniques for power system dynamic control, particularly voltage and frequency control. The conventional control strategies used in power systems are complex and time-consuming due to the complicated high-order nonlinearities of the system. RL, which is a type of neural network-based technique, has shown promise in solving these complex problems by fitting any nonlinear system with the proper network structure. The proposed RL algorithm, called Guided Surrogate Gradient-based Evolution Strategy (GSES) determines the weights of the policy (which generates the action for our control reference signal) without back-propagation process for gradient update using a simultaneous perturbation stochastic approximation approach comparing to many other RL algorithms, thus it achieves a much faster and more robust learning convergence. It is introduced and implemented in three different power system scenarios: High Voltage Direct Current (HVDC) based inter-area oscillation damping system, Doubly-fed Induction Generator (DFIG) based Fault-Ride-Through (FRT) system, and modified IEEE-39 Bus based frequency regulation system. In the case of the HVDC-based system, the proposed GSES-based power oscillation damping control approach overcomes the challenges of setting optimal controller parameters of the HVDC under various system transient events. This approach is also shown to be superior to conventional power oscillation damping methods. Further, the GSES algorithm is found to be effective in controlling the DFIG power and capacitor DC-link voltage, which helps prevent the rotor of DFIG from over-current risk and maintain the grid-connected operation. Finally, the proposed RL-based solution for frequency response in wind farms is tested on a modified IEEE-39 bus system and is found to reliably support the frequency of the power system and prevent unnecessary load shedding. Overall, this dissertation shows the potential of RL-based techniques in power system dynamic control, particularly frequency control, and provides evidence for the effectiveness of the GSES algorithm in various power system scenarios. The use of RL in power systems could lead to more efficient and effective control strategies during contingencies, which is crucial in maintaining the stability of today’s large, high-order nonlinear dynamic power systems