562 research outputs found
Evaluating dynamic partial reconfiguration in the integer pipeline of a FPGA-based opensource processor
No full textThis work explores the potential of sharing different arithmetic hardware operators tightly coupled to the integer pipeline of the open-source LEON3 processor. The idea is to map these modules to the same silicon area saving power consumption and area utilisation. The same strategy can be used to extend the architecture of processors optimized for applications with specific energy constraints. The proposed platform serves as a guideline to illustrate gains obtained through partial reconfiguration that need to adapt to changing standards and protocols with a limited number of resources.This work explores the potential of sharing different arithmetic hardware operators tightly coupled to the integer pipeline of the open-source LEON3 processor. The idea is to map these modules to the same silicon area saving power consumption and area utilisation. The same strategy can be used to extend the architecture of processors optimized for applications with specific energy constraints. The proposed platform serves as a guideline to illustrate gains obtained through partial reconfiguration that need to adapt to changing standards and protocols with a limited number of resources
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning
No full textIn modern highly interconnected power grids, automatic generation control
(AGC) is crucial in maintaining the stability of the power grid. The dependence
of the AGC system on the information and communications technology (ICT) system
makes it vulnerable to various types of cyber-attacks. Thus, information flow
(IF) analysis and anomaly detection became paramount for preventing cyber
attackers from driving the cyber-physical power system (CPPS) to instability.
In this paper, the ICT network traffic rules in CPPSs are explored and the
frequency domain features of the ICT network traffic are extracted, basically
for developing a robust learning algorithm that can learn the normal traffic
pattern based on the ResNeSt convolutional neural network (CNN). Furthermore,
to overcome the problem of insufficient abnormal traffic labeled samples,
transfer learning approach is used. In the proposed data-driven-based method
the deep learning model is trained by traffic frequency features, which makes
our model robust against AGC's parameters uncertainties and modeling
nonlinearities.Comment: Editor: Geert Deconinck. 18th European Dependable Computing
Conference (EDCC 2022), September 12-15, 2022, Zaragoza, Spain. Fast Abstract
Proceedings - EDCC 202
The mirror box illusion : Manipulation of visual information during bimanual coordination in children with and without spastic hemiparetic cerebral palsy
No full textSavelsbergh, G.J.P. [Promotor]Ledebt, A. [Copromotor]Deconinck, F.J.A. [Copromotor
Mirror (a)symmetry?: Visuo-proprioceptive interactions in individuals with Spastic Hemiparetic Cerebral Palsy
No full textSavelsbergh, G.J.P. [Promotor]Ledebt, A. [Copromotor]Deconinck, F.J.A. [Copromotor
Local solutions of the optimal power flow problem
No full textThe existence of locally optimal solutions to the AC optimal power flow problem (OPF) has been a question of interest for decades. This paper presents examples of local optima on a variety of test networks including modified versions of common networks. We show that local optima can occur because the feasible region is disconnected and/or because of nonlinearities in the constraints. Standard local optimization techniques are shown to converge to these local optima. The voltage bounds of all the examples in this paper are between 5% and 10% off-nominal. The examples with local optima are available in an online archive (http://www.maths.ed.ac.uk/optenergy/LocalOpt/) and can be used to test local or global optimization techniques for OPF. Finally we use our test examples to illustrate the behavior of a recent semi-definite programming approach that aims to find the global solution of OPF
Special Section on Harmonization of Multi-energy Systems in CSEE Journal of Power and Energy Systems
No full textstatus: Publishe
Synergies between energy supply networks
No full textThe increasing share of variable renewable energy sources, strict targets set for the reduction of greenhouse gas emissions and the requirements on improvement of system security and reliability are calling for important changes in our energy systems. Energy systems have been in transition, extending their boundaries beyond the energy systems themselves, the 3-D interactive extensions, that relate to the dimensions of physical Space, Time scale and Human behaviors – STH extension. Under the new circumstance of the STH-demission, we need new approaches and solutions to solve the challenging issues associated with new transitions of future clean energy systems [1]. The next generation of competitive technologies and services that will create or enhance synergies between energy supply networks are being developed and matured. Facing these challenges and opportunities, energy supply networks (e.g. electric power networks, natural gas networks, hydrogen production and transportation, district heating and cooling systems, electrified transportation, and the associated information and communication infrastructure) are undergoing a radical transformation with massive investments in infrastructure and technologies [2]. This provides a window of opportunity. This transition is significantly increasing the coupling and interactions between energy supply networks via network coupling technologies, e.g. Combined Heat and Power units (CHP), Power to Gas (e.g. using excess renewable energy to produce hydrogen, which can be injected to the gas network or converted to synthetic natural gas, SNG, and then injected into the gas network) and Power to heat (e.g. heat pumps) processes. There is an urgent need to develop the next generation network coupling technologies and energy system integration methods which will make optimal use of synergies between energy networks to increase the hosting capacity and flexibility of distributed energy resources (DERs), enhanced demand response and support Smart Grid operation
Beoordeling van foutstroombijdragestrategieën door vermogenelektronische gedistribueerde bronnen
No full textIt is a clear trend that the share of Distributed Generation (DG) in the grid increases. Many DG units are Converter Based DG (CBDG) units and it is expected that the number of CBDG units will increase in the future. Due to the variable nature of most of these CBDG units, there will be occasions when the share of CBDG units is much higher than their average share in the energy production. During these periods, several of the conventional synchronous generators will be disconnected from the grid and scenarios with a large number of CBDG units and very few synchronous generators arise.
The goal of this dissertation is to evaluate the impact of these scenarios on the fault currents and voltages during balanced and unbalanced faults. On the one hand, fault currents have a direct impact on the protection system of the grid. On the other hand, the grid voltages during a fault determine the impact of the fault on the loads and the generation units. Therefore, both the fault currents and fault voltages strongly influence the reliability of the grid.
First, it is demonstrated that existing control systems of CBDG units are able to flexibly control the fault current injections of CBDG units. Thus, their fault behaviour is a design parameter. Several specific control aspects related to the injection of negative sequence currents during faults are explained.
Since detailed electromagnetic transient simulations are considered computationally too intensive for scenarios with multiple CBDG units, this dissertation investigates which simplified method is able to evaluate the fault currents and fault voltages in these scenarios. As the fault behaviour of CBDG units is a design parameter, it is clear that any method has to take into account the control objectives of the CBDG units. Based on these requirements, a simplified calculation framework is developed and validated. This framework is then used to evaluate different current contribution strategies during balanced and unbalanced faults in scenarios with a high share of CBDG.
For balanced faults, the influence of different voltage support settings on the short-circuit power in the grid is investigated. It is shown that CBDG units, with the appropriate voltage support settings, can contribute to the short-circuit power of the grid. This way, the CBDG units limit the drop in short-circuit power at the higher voltage levels when they replace conventional generation. When this reduction of the short-circuit power at the higher voltage levels is limited, the fault currents at the lower voltage levels, supplied by the higher voltage levels, do not change significantly. This avoids a complete redesign of the existing protection systems of typical European medium and low voltage grids in scenarios with a high share of CBDG and little conventional generation, as these protection systems rely on the magnitude of the fault currents. Locally, the voltage support of CBDG units can result in a (limited) increase of the short-circuit power. The voltage support can then be applied until the short-circuit power limits of the local grid are reached.
For unbalanced faults, scenarios with a high share of CBDG and little conventional generation are evaluated. When only positive sequence voltage support is applied, and negative sequence currents are blocked by the CBDG units, this can lead to very low fault currents during unbalanced faults. These low fault currents would require a complete redesign, including huge investment costs, of the existing protection systems at the lower voltage levels of the grid, as these protection systems rely on the magnitude of the fault currents. In addition, very low fault currents result in a reduced reliability of the grid, as faults at lower voltage levels then have a significant impact on the higher voltage levels. The remaining synchronous generators in the grid also experience additional stress when CBDG units only provide positive sequence voltage support during unbalanced faults. When CBDG units provide both positive and negative sequence voltage support, these drawbacks are avoided: the existing protection systems of typical European medium and low voltage grids do not require a complete redesign, faults on the lower voltage levels do not have a significant impact on the higher voltage levels and the remaining synchronous generators don't experience additional stress.status: Publishe
Peer-to-peer-gebaseerde netwerkondersteuningsfunctionaliteit voor intelligente omvormers — Gedistribueerde spannings- en frequentieregeling
No full textControl of distribution networks are facing significant challenges with the increasing penetration of distributed energy resources. The focus of this thesis is to develop active voltage control systems provided by photovoltaic (PV) and PV-battery inverters to mitigate or eliminate voltage problems of distribution networks. Various approaches that combine local and centralized voltage control techniques have been proposed in literature. These techniques suffer from different problems; centralized voltage control systems have poor reliability and scalability; and local voltage control systems suffer from degraded performance. To overcome the drawbacks of centralized and local voltage control systems, this thesis develops novel peer-to-peer-based grid voltage support functions (P2P-based GVSFs). Smart inverters equipped with P2P-based GVSFs interact with each other in a P2P fashion and form a distributed voltage control (DVC) system. Two novel methodologies are proposed in the thesis to design a P2Pbased DVC system. The first one is based on real-time distributed optimization, while the second one is based on offline robust optimization.
In the first methodology, two gossip-based decomposition techniques are developed: the fully distributed Dual Decomposition (DD) method and the Jacobi-Proximal Alternating Direction Method of Multipliers (JP-ADMM). The fully distributed DD algorithm allows for simple implementation, but in most cases requires large number of iterations to converge. Results show that this algorithm can be used to implement a DVC system with only few agents (e.g. 10 agents or less). The JP-ADMM algorithm requires more local computations and communication; yet it converges much faster than the DD algorithm. The JP-ADMM-based DVC algorithm with 50 agents, for example, needs around 1.25 minutes to converge, whereas for the same number of agents, the DD-based DVC algorithm needs around 59.4 minutes to converge. To experimentally validate the performance of the real-time optimization-based DVC system, the thesis develops a novel laboratory-based P2P voltage control testbed.
There are several challenges for the decomposition-based voltage control technique. First major issue is its vulnerability to control instability due to inappropriate control parameters tuning. Second, implementing a real-time DVC system using decomposition algorithms requires the GVSFs to solve a complex optimization problem every iteration (e.g. every 5 s). This motivates studying the simplification of the implementation of DVC systems. In the second methodology, the thesis simplifies the implementation of a DVC system and reduces its computational and communication burden by applying robust optimization to learn linear voltage control policies on a day-ahead basis. In doing so, the inverters communicate with each other in real-time and solve only a set of linear equations to control voltages in a distributed manner.
In the final phase of this work, the thesis combines policy-based DVC system with frequency containment reserve (FCR), to increase value from PV-battery systems. To this end, the thesis develops a robust mathematical optimization program that enables PV-battery systems to simultaneously provide FCR service (to their synchronous area), and voltage regulation service (to their distribution network).status: Publishe
Strategic Behaviour in Power Wholesale Electricity Markets: Design, Implementation & Validation of an Agent-based Simulation Platform
No full textLiberalising the European electricity industry did not naturally produce its intended results. Network constraints, few dominant sellers in a relatively small market, complex market designs, price-inelastic consumers, reductions in generation capacity, unavailability of perfect information provided in real-time, and portfolio economics and technical characteristics induced the observed strategic gaming behaviour of generators.In order to understand the evolution of the electricity market, dynamic market modelling tools can be applied. Using such models, all stakeholders can gain insights on the sensitivity of market design parameters against potential disturbances or market imperfections, and take necessary actions to pro-actively address them. How the state of an interconnected electrical system evolves after clearing the day-ahead market as organised under the European Power Exchange model, subject to strategic gaming behaviour has been studied. Presented contributions revolve around two research domains.Firstly, a novel profit risk hedging offering strategy is presented. It submits the coordinated dispatch schedule of thermal, hydropower and renewable power plants to the market operator. The generator pursues a total profit-maximising objective by simultaneously exercising physical and economic withholding while explicitly taking into account underlying technical constraints and plant economics. Price-responsive demand is realistically modelled by step-wise decreasing curves. The consideration of portfolio flexibility to mitigate profit risks is proven to yield higher total profit than alternative strategies.Secondly, the offering strategy is integrated in a newly designed dynamic electricity market model. Using multi-agent systems, each generator updates its perception of the market environment by evaluating the performance of historic decisions on its profit. Four learning and decision processes have been designed. The first determines the optimal renewable energy supply quantity to submit with hydropower as reserve, in order to minimise future self-balancing responsibilities. The second determines whether to behave competitively or strategically. The third determines the degree to which the generator can strategically increase its profit. The last accounts for crossborder exchanges.Results obtained by applying the model to case studies illustrate its validity. Consequently, by explicitly taking into account the most relevant market design parameters, the agent-based simulation platform is capable of answering research questions existing electricity market simulation tools cannot address.status: Publishe
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