Jesús Fraile-Ardanuy

Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms

This paper presents two different power system stabilizers (PSSs) which are designed making use of neural fuzzy network and genetic algorithms (GAs). In both cases, GAs tune a conventional PSS on different operating conditions and then, the relationship between these points and the PSS parameters is learned by the ANFIS. ANFIS will select the PSS parameters based on machine loading conditions. The first stabilizer is adjusted minimizing an objective function based on ITAE index, while second stabilizer is adjusted minimizing an objective function based on pole-placement technique. The proposed stabilizers have been tested by performing simulations of the overall nonlinear system. Preliminary experimental results are shown.

Variable-speed hydro generation: operational aspects and control

The potential advantages of variable-speed hydroelectric generation are discussed in this article. Some general aspects concerning the efficiency gains in turbines and the improvements in plant operation are analyzed. The main results of measurements on a test loop with an axial-flow turbine are reported. Also, we describe the control scheme implemented, which is based on artificial neural networks. To confirm the practical interest of this technology, the operation of a run-of-the-river small hydro plant has been simulated for several years. Substantial increases in production with respect a fixed-speed plant have been found.

Determining Electric Vehicle Charging Point Locations Considering Drivers’ Daily Activities

Abstract In this paper the daily temporal and spatial behavior of electric vehicles (EVs) is modelled using an activity-based (ActBM) micro-simulation model for Flanders region (Belgium). Assuming that all EVs are completely charged at the beginning of the day, this mobility model is used to determine the percentage of Flemish vehicles that cannot cover their programmed daily trips and need to be recharged during the day. Assuming a variable electricity price, an optimization algorithm determines when and where EVs can be recharged at minimum cost for their owners. This optimization takes into account the individual mobility constraint for each vehicle, as they can only be charged when the car is stopped and the owner is performing an activity. From this information, the aggregated electric demand for Flanders is obtained, identifying the most overloaded areas at the critical hours. Finally it is also analyzed what activities EV owners are underway during their recharging period. From this analysis, different actions for public charging point deployment in different areas and for different activities are proposed.

Adaptive Power System Stabilizer Using ANFIS and Genetic Algorithms

This paper presents an adaptive Power System Stabilizer (PSS) using an Adaptive Network Based Fuzzy Inference System (ANFIS) and Genetic Algorithms (GAs). Firstly, genetic algorithms are used to tune a conventional PSS on a wide range of operating conditions and then, the relationship between these operating points and the PSS parameters is learned by the ANFIS. The ANFIS optimally selectes the classical PSS parameters based on machine loading conditions. The proposed stabilizer has been tested by performing nonlinear simulations using a synchronous machine-infinite bus model. The results show the robustness and the capability of the stabilizer to enhance system damping over a wide range of operating conditions and system parameter variations.

Peer to Peer Energy Trading with Electric Vehicles

This paper presents a novel peer-to-peer energy trading system between two sets of electric vehicles, which significantly reduces the impact of the charging process on the power system during business hours. This trading system is also economically beneficial for all the users involved in the trading process. An activity-based model is used to predict the daily agenda and trips of a synthetic population for Flanders (Belgium). These drivers can be initially classified into three sets; after discarding the set of drivers who will be short of energy without charging chances due to their tight schedule, we focus on the two remaining relevant sets: those who complete all their daily trips with an excess of energy in their batteries and those who need to (and can) charge their vehicle during some daily stops within their scheduled trips. These last drivers have the chance to individually optimize their energy cost in the time-space dimensions, taking into account the grid electricity price and their mobility constraints. Then, collecting all the available offer/demand information among vehicles parked in the same area at the same time, an aggregator determines an optimal peer-to-peer price per area and per time slot, allowing customers with excess of energy in their batteries to share with benefits this good with other users who need to charge their vehicles during their daily trips. Results show that, when applying the proposed trading system, the energy cost paid by these drivers at a specific time slot and in a specific area can be reduced up to 71%.

Control of a run of river small hydro power plant

Nowadays, new small hydro power plants are being built all over the world. In most cases, these power plants lack significant storage capacity; therefore the more adequate control strategy is to keep constant the water level at the intake basin to capture the maximum amount of energy from the river flow. In this paper, the control of a diversion run of river hydro plant is considered. A practical criterion for tuning the gains of the PI controller is proposed. The influence of turbine operating point on controller gains is analyzed. Finally, the results are applied to a model of a typical hydropower plant to verify that the tuning of the PI controller allows a stable and suitable control of the power plant.

Neural adaptive control of non‐linear plants via a multiple inverse model approach

In this paper, neural architectures for controlling non-linear plants with parameter variation are proposed. In the first part of the document, the concept of specialized learning over an operation region is considered in order to identify the inverse dynamics of a given plant. Some aspects concerning discretization and invertibility of continuous-time plants are also addressed. In the second part of this work, a control architecture which combines the former approach of inverse identification through specialised learning with a multiple model scheme is presented. Finally, simulation results are discussed, evaluating the performance of the proposed schemes; specifically, the presented controllers are applied to the simulation of the control of a robot arm. Copyright

Vehicle to vehicle energy exchange in smart grid applications

This paper presents a novel vehicle to vehicle energy exchange market (V2VEE) between electric vehicles (EVs) for decreasing the energy cost to be paid by some users whose EVs must be recharged during the day to fulfil their daily scheduled trips and also reducing the impact of charging on the electric grid. EVs with excess of energy in their batteries can transfer this energy among other EVs which need charge during their daily trips. These second type of owners can buy the energy directly to the electric grid or they can buy the energy from other EV at lower price. An aggregator is responsible for collecting all information among vehicles located in the same area at the same time and make possible this energy transfer.

Improving Understanding of Single Phase Transformer Behaviour through a Multimedia Tool

Many civil engineering students may show little motivation for a course in electrical machinery, because they consider it unexciting and of little importance to their professional practice. Therefore, the use of interactive computer graphics and animations can be a complementary tool for teaching these topics. This paper describes the use of Multimedia MATLAB Graphical User Interface (GUI) for studying the electric single phase transformer at undergraduate level. This Multimedia tool integrates: a description of its structural features; a video recorded in the laboratory showing the open circuit and short circuit tests; and a simulation module that allows us to perform these transformer tests and to analyse voltage regulation and the efficiency of the transformer. This GUI helps students not only to get a better understanding of the advantages and limitations of computer simulation, but also to get a better feel for the physical concepts they study in the lecture and laboratory parts of the course.

Characteristic study, its identification and self-tuned approach to control hydro-power plants

The water time constant and mechanical time constant greatly influences the power and speed oscillations of hydro-turbine-generator unit. This paper discusses the turbine power transients in response to different nature and changes in the gate position. The work presented here analyses the characteristics of hydraulic system with an emphasis on changes in the above time constants. The simulation study is based on mathematical first-, second-, third- and fourth-order transfer function models. The study is further extended to identify discrete time-domain models and their characteristic representation without noise and with noise content of 10 & 20 dB signal-to-noise ratio (SNR). The use of self-tuned control approach in minimising the speed deviation under plant parameter changes and disturbances is also discussed.