Artículos (Ingeniería Eléctrica)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11352

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A Comparison Between Kalman Filters and STDFT for Harmonic Estimation in Power Systems
(World Scientific and Engineering Academy and Society (WSEAS), 2005) Rosendo Macías, José Antonio; Gómez Expósito, Antonio; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
This paper presents a comparison between Kalman filter and the running DFT for the computation of harmonics in power systems applications. The performance of both filters is compared for events like voltage dips or those in which a decaying DC component is present. The comparison considers also the presence of higher order harmonics.
A Comprehensive Centralized Approach for Voltage Constraints Management in Active Distribution Grid
(Institute of Electrical and Electronics Engineers (IEEE), 2014) Capitanescu, Florin; Bilibín, Ilya; Romero Ramos, Esther; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
This paper deals with the management of voltage constraints in active distribution systems that host a significant amount of distributed generation (DG) units. To this end we propose a centralized optimization approach which aims at minimizing the amount of MW curtailment of non-firm DG to remove voltage constraints. The salient feature of this approach is that it comprehensively and properly models the full variety of possible control means (i.e., DG active/reactive power including DG shut-down, on load tap changing transformer ratio, shunt capacitor, and remotely controlled switches or breakers), most of which having a discrete behavior. We develop and compare the performances of two optimization models on a snapshot basis for various distribution systems up to 1089 buses. In particular we show that the use of remotely controlled switches so as to transfer DG between feeders in case of voltage constraints may lead to significant reduction of the DG curtailment.
A Comprehensive VSG-based Onshore FRT Control Strategy for OWFs with VSC-MT-HVDC Transmission
(Institute of Electrical and Electronics Engineers, 2017) Seyed Saeid Heidary, Yazdi; Jafar, Milimonfared; Seyed Hamid, Fathi; Rouzbehi, Kumars; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
This paper proposes a communication-free control strategy at the offshore wind farm (OWF) level to enhance onshore fault ride-through (FRT) grid code compliance of the voltage source converter (VSC)-based multi-terminal high voltage direct current (MT-HVDC) grid. In this proposal, the emerging virtual synchronous generator (VSG) concept is employed to equip the Type 4 wind turbine generator (WTG)s with inherent grid forming ability. Accordingly, it is proposed to switch the offshore HVDC converters control mode from grid forming to grid feeding during onshore FRT period to realize direct wind power in-feed reduction as a function of the severity of MT-HVDC grid's overvoltage. The related dynamics are mainly characterized by the high-speed current control loop, so improved OWF response is achieved during onshore FRT period as conventional voltage/frequency modulation strategies are not employed. New analysis/amendments are also proposed to study and improve the transient active power reduction sharing between the WTGs in first few power cycles under wind wake effect. Finally, with the objective of a smooth transfer of HVDC converters and WTGs in several proposed operation states, a set of state machines are proposed considering whole WTG's dynamics. Comprehensive time-domain simulations are performed with averaged electromagnetic transient models to demonstrate the improved onshore FRT behavior in terms of minimizing the electrical stress at both MT-HVDC grid and OWF levels.
A coordinated control of offshore wind power and bess to provide power system flexibility
(MDPI AG, 2021) Acosta M. N.; González - Longatt, F.; Roldán Fernández, Juan Manuel; Burgos Payán, Manuel; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
The massive integration of variable renewable energy (VRE) in modern power systems is imposing several challenges; one of them is the increased need for balancing services. Coping with the high variability of the future generation mix with incredible high shares of VER, the power system requires developing and enabling sources of flexibility. This paper proposes and demonstrates a single layer control system for coordinating the steady‐state operation of battery energy storage system (BESS) and wind power plants via multi‐terminal high voltage direct current (HVDC). The proposed coordinated controller is a single layer controller on the top of the power converter‐based technologies. Specifically, the coordinated controller uses the capabilities of the distributed battery energy storage systems (BESS) to store electricity when a logic function is fulfilled. The proposed approach has been implemented considering a control logic based on the power flow in the DC undersea cables and coordinated to charging distributed‐BESS assets. The implemented coordinated controller has been tested using numerical simulations in a modified version of the classical IEEE 14‐bus test system, including tree‐HVDC converter stations. A 24‐h (1‐min resolution) quasi-dynamic simulation was used to demonstrate the suitability of the proposed coordinated control. The controller demonstrated the capacity of fulfilling the defined control logic. Finally, the instan-taneous flexibility power was calculated, demonstrating the suitability of the proposed coordinated controller to provide flexibility and decreased requirements for balancing power.
A fast non-decoupled algorithm to solve the load flow problem in meshed distribution networks
(Elsevier, 2022-12) Hernández Fuentes, Hugo Edgardo; Zarco Soto, Francisco Javier; Martínez Ramos, José Luis; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Centro para el Desarrollo Tecnológico Industrial; Universidad de Sevilla. TEP-196: Sistemas de Energía Eléctrica
The purpose of this work is to compare the classical methods of power flow resolution (Newton–Raphson and Gauss–Seidel) with a more recent algorithm known as Alternating Search Direction (ASD), for which its equations, the steps to follow and the parameters to consider are described. In addition, a series of tests are carried out in different distribution networks where the reduction of execution time, accuracy, and robustness of the presented algorithm is demonstrated, taking as a reference the behavior of the well-known Newton–Raphson algorithm. Finally, the advantage of selecting certain parameters in the ASD algorithm is studied.
A fitting procedure for probability density functions of service restoration times. Application to underground cables in medium-voltage networks
(Elsevier, 2023-04) Clavijo-Blanco, José Antonio; González Cagigal, Miguel Ángel; Rosendo Macías, José Antonio; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
Distribution companies have the responsibility to provide a quality service to their customers, according to the existing regulation. Reliability issues, such as power outages, are registered in databases for a quantitative evaluation of this quality. This paper uses one of these historical records to make a statistical analysis of service restoration times, applied to the particular case of underground cables in medium voltage networks. An algorithm is proposed to fit the raw data to the probability density functions typically used in reliability analysis. The best-fitted distribution is determined in each case according to the information provided by a set of goodness-of-fit tests. Different groups are considered for the elements of the systems, concerning their functionality and voltage level. The presented procedure is applied to an electrical network with more than 350 feeders. Results have been obtained globally, showing that the observed service restoration time is lower than the estimated maximum limit in 98.00% of cases. The probability functions provided by the proposed algorithm can be used to improve the accuracy of the reliability models for the electric power system.
A holistic model-less approach for the optimal real-time control of power electronics-dominated AC microgrids
(Elsevier, 2023-04) Olives Camps, Juan Carlos; Rodríguez del Nozal, Álvaro; Mauricio Ferramola, Juan Manuel; Maza Ortega, José María; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla, Spain in the framework of VI PPIT-US; Spanish Ministry of Science and Innovation under grant no. PID2021-124571OB-I00; Junta de Andalucía, Spain under the project FLEX-REN (P18-TP-3655); CERVERA research programme of CDTI, Spain, the Industrial and Technological Development Centre of Spain, under the research Project HySGrid+ (CER-20191019); H2020 under SUNRISE project grant agreement 101079200; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
This paper addresses the problem of optimally operating a set of grid-forming devices in an AC microgrid when a detailed network model is not available. The main aim of the approach is to maximize the power sharing of the controllable grid-forming devices and to maintain the frequency and the nodal voltages of the microgrid as close as possible to their corresponding references. The proposed control architecture is conformed by a local control layer in each grid-forming device that intends to emulate the performance of a synchronous machine and a centralized secondary controller composed of two complementary tools that coordinates the setpoints of the grid-forming devices: an online feedback optimization algorithm and an automatic generation control. The proposed method has been validated through simulations and hardware-in-the-loop tests, evidencing its good performance and robustness under different conditions.
A holistic state estimation framework for active distribution network with battery energy storage system
(IEEE, 2022) Song, Shaojian; Wei, Huangjiao; Lin, Yuzhang; Wang, Cheng; Gómez Expósito, Antonio; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
Battery energy storage systems (BESSs) are expect‐ ed to play a crucial role in the operation and control of active distribution networks (ADNs). In this paper, a holistic state esti‐ mation framework is developed for ADNs with BESSs integrat‐ ed. A dynamic equivalent model of BESS is developed, and the state transition and measurement equations are derived. Based on the equivalence between the correction stage of the iterated extended Kalman filter (IEKF) and the weighted least squares (WLS) regression, a holistic state estimation framework is pro‐ posed to capture the static state variables of ADNs and the dy‐ namic state variables of BESSs, especially the state of charge (SOC). A bad data processing method is also presented. The simulation results show that the proposed holistic state estima‐ tion framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs, providing comprehensive situational awareness for the whole system.
A hybrid methodology for optimal var dispatch in the western algerian power system
(Academiei române, 2012) Khiat, Mounir; Marano-Marcolini, Alejandro; Chetti, Saliha; Martínez Ramos, José Luis; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
This paper describes the methodology adopted for the optimal var dispatch (OVD) integrating a particle swarm optimization (PSO) algorithm and the interior point method (IPM). The proposed hybrid method can be mainly divided in two parts. The first part is to solve the OVD with the IPM based on the logarithmic- barrier primal- dual algorithm (LB-PDA), for non linear programming (NLP) by relaxing the discrete variables. In the second part, the PSO algorithm is used to solve the discrete variables with the continuous variables being fixed, whereas the IPM solves the continuous optimization with the discrete variables being constant. The optimal solution can be obtained by solving the two sub-problems alternately. Numerical simulation on the Western Algerian power system illustrate this proposed hybrid method.
A Hybrid Procedure Including Subtransmission Systems and Substations for Reliability Assessment
(IEEE Xplore Digital Library, 2013) Martínez-Lacañina, Pedro José; Villa Jaén, Antonio de la; Martínez Ramos, José Luis; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Gómez Expósito, Antonio; Universidad de Sevilla. TEP196: Sistemas Eléctricos de Potencia
A new procedure focused on reliability analysis of subtransmission systems supported by the state enumeration technique is presented. This new methodology is conducted in three stages. First, a classical state enumeration reliability assessment is performed for the branch-node model of a subtransmission system, assuming that substations are perfectly reliable. Second, a detailed model of the subtransmission system is considered and the reliability of each substation is assessed by considering them in a “one-by-one” process, supposing perfect operation for the branch-node model. Finally, the reliability indices calculated in the ﬁrst and second stages are analytically combined to obtain the reliability indices for the subtransmission system (system reliability indices) and for the load nodes of the distribution system (load-node reliability indices). Test results show that the proposed methodology is suitable for both planning studies and 24 hours-ahead security assessment.
A Low-Cost Non-Intrusive Method for In-Field Motor Speed Measurement Based on a Smartphone
(MDPI, 2021-06) Páramo Balsa, Paula; Roldán Fernández, Juan Manuel; Burgos Payán, Manuel; Riquelme Santos, Jesús Manuel; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; CYTED Network Program grant 718RT0564; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
Induction motors are broadly used as drivers of a large variety of industrial equipment. A proper measurement of the motor rotation speed is essential to monitor the performance of most industrial drives. As an example, the measurement of rotor speed is a simple and broadly used industrial method to estimate the motor’s efficiency or mechanical load. In this work, a new low-cost non-intrusive method for in-field motor speed measurement, based on the spectral analysis of the motor audible noise, is proposed. The motor noise is acquired using a smartphone and processed by a MATLAB-based routine, which determines the rotation speed by identifying the rotor shaft mechanical frequency from the harmonic spectrum of the noise signal. This work intends to test the hypothesis that the emitted motor noise, like mechanical vibrations, contains a frequency component due to the rotation speed which, to the authors’ knowledge, has thus far been disregarded for the purpose of speed measurement. The experimental results of a variety of tests, from no load to full load, including the use of a frequency converter, found that relative errors on the speed estimation were always lower than 0.151%. These findings proved the versatility, robustness, and accuracy of the proposed method.
A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case
(Elsevier, 2023-12) Riquelme Domínguez, José Miguel; Carranza García, Manuel; Lara Benítez, Pedro; González Longatt, Francisco; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. TIC-134: Sistemas informáticos
The progressive substitution of conventional synchronous generation for renewable-based generation imposes a series of challenges in many aspects of modern power systems, among which are the issues related to the low rotational inertia systems. Rotational inertia and the kinetic energy stored in the rotating masses in the power system play a fundamental role in the operation of power systems as it represents in some sort the ability of the system to withstand imbalances between generation and demand. Therefore, transmission system operators (TSOs) need tools to forecast the inertia or the kinetic energy available in the systems in the very short term (from minutes to hours) in order to take appropriate actions if the values fall below the one that ensures secure operation. This paper proposes a methodology based on machine learning (ML) techniques for short-term kinetic energy forecasting available in power systems; it focuses on the length of the moving window, which allows for obtaining a balance between the historical information needed and the horizon of forecasting. The proposed methodology aims to be as flexible as possible to apply to any power system, regardless of the data available and the software used. To illustrate the proposed methodology, time series of the kinetic energy recorded in the Nordic Power System (NPS) has been used as a case study. The results show that Linear Regression (LR) is the most suitable method for a time horizon of one hour due to its high accuracyto-simplicity ratio, while Long Short-Term Memory (LSTM) is the most accurate for a forecasting horizon of four hours. Experimental assessment has been carried out using Typhoon HIL-404 simulator, verifying that both algorithms are suitable for real-time simulation.
A Market-Based Analysis on the Main Characteristics of Gearboxes Used in Onshore Wind Turbines
(MDPI, 2017-10-25) Vázquez-Hernández, Cristina; Serrano-González, Javier; Centeno Báez, Gabriel; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. Departamento de Ingeniería Mecánica y de Fabricación
Even though wind energy is one of the most mature renewable technologies, it is in continuous development not only because of the trend towards larger wind turbines but also because of the development of new technological solutions. The gearbox is one of the components of the drive train in which the industry is concentrating more effort on research and development. Larger rotor blades lead to more demanding requirements for this component as a consequence of a higher mechanical torque and multiplication ratio (due to lower rotational speed of blades while the rotational speed on the generator side remains at similar values). In addition, operating conditions become increasingly demanding in terms of reliability, performance, and compactness. This paper analyses the different gearbox arrangements that are implemented by manufacturers of onshore wind turbines, as well as their market penetration (including different aspects that affect the design of the gearbox, such as drive train configuration and turbine size). The analysis carried out shows a clear convergence towards gearboxes with three stages. However, there is a noticeable diversity in the types of gears used, depending to a large extent on the preferences of each manufacturer but also on the nominal power of the wind turbine and drive train configuration.
A model-less control algorithm of DC microgrids based on feedback optimization
(Elsevier, 2022-10) Olives Camps, Juan Carlos; Rodríguez del Nozal, Álvaro; Mauricio Ferramola, Juan Manuel; Maza Ortega, José María; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
This work addresses the problem of the optimal real-time control of a DC microgrid without relying on its corresponding network model. The main goal of such a controller is to keep the nodal network voltages within the regulatory limits while offering current sharing capability between the different controllable generators powering the DC microgrid. The proposed model-less methodology is based on feedback optimization, which takes advantage of the available real-time measurements to update the setpoints of the DC generation assets. The optimal control variables are determined in an iterative manner by applying a primal–dual saddle-point method, which guarantees appropriate convergence features. The paper details both centralized and distributed implementations which are compared through simulations. The results evidence a good dynamic performance and an optimal steady-state operation as the proposed control algorithm converges to the solution provided by a conventional model-based Optimal Power Flow.
A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization
(MDPI, 2019-11) Rodríguez del Nozal, Álvaro; Gutiérrez Reina, Daniel; Alvarado-Barrios, Lázaro; Tapia Córdoba, Alejandro; Escaño González, Juan Manuel; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.
A neural network-based classifier for identifying and locating neutral wire breaks in low voltage distribution networks
(Elsevier, 2024-09) Carmona Pardo, Rubén; Rodríguez del Nozal, Álvaro; Romero Ramos, Esther; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
The breakage of the neutral conductor in low voltage distribution networks is a major concern for distribution companies. This breakage causes significant voltage deviations that can damage the connected equipment as well as jeopardizing people. The detection and localization of the breakage is a major challenge as it does not always manifest in the same way. This work presents a methodology based on artificial intelligence for the detection and localization of neutral conductor breaks in distribution networks. Two neural networks are trained in attempt to solve each of the challenges. For this purpose, measurements commonly taken by smartmeters such as power and nodal voltages are used. The methodology is evaluated in simulation exhibiting a good performance.
A new DC corrective OPF based on generator and branch outages modelled as ﬁctitious nodal injections
(IEEE Xplore Digital Library, 2014) Martínez-Lacañina, Pedro José; Martínez Ramos, José Luis; Villa Jaén, Antonio de la; Marano-Marcolini, Alejandro; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. TEP196: Sistemas Electricos de Potencia
This work deals with a new formulation for the direct current corrective optimal power ﬂow. The formulation is based on the outage of generators and/or branches modelled as ﬁctitious injections of active power. By including that ﬁctitious injections in the optimization problem, the injections are adjusted to the post-contingency state as a consequence of the corrective actions carried out to bring the system back to its normal state. So, when the analysis of contingencies is performed, the classical topological analysis and the subsequent analyses are avoided with this approach. This new formulation uses the sensitivity matrix between branch power ﬂows and powers injected in a power system. An important feature of this matrix is to remain constant during the Contingency Analysis performed for the generation-load scenario (base case) of each period of time to be analysed. The approach proposed is illustrated in the IEEE-RTS of 24buses. The results obtained in this distribution network demonstrate that the proposed methodology can assess the impact of contingencies with an acceptable accuracy and a short computation time.
A New Droop Coefficient Design Method for Accurate Power-Sharing in VSC-MTDC Systems
(IEEE, 2019-04) Liu, Yuchao; Green, Tim C.; Wu, Jian; Rouzbehi, Kumars; Raza, Ali; Xu, Dianguo; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; National Natural Science Foundation of China
This paper proposes a new droop coefficient design method with the aim of improving the power-sharing accuracy among the converters in a multi-terminal dc (MTDC) system. The proposed droop coefficient design method works by adjusting the droop coefficient and can realize an arbitrary power-sharing ratio among all the converters in an MTDC system. This method does not rely on a communication network and therefore has the potential for higher reliability than the alternative methods. Mitigating the impact of the variation of dc line resistances on the power-sharing is discussed. Simulation of a four-terminal MTDC system is carried out by using PSCAD/EMTDC. The experimental results under a scaled-down four-terminal dc grid platform demonstrate the effectiveness of the proposed method.
A Non-Cooperative Game-Theoretic Approach for Distributed Voltage Regulation in DC Grids with a High Penetration of Renewable Energies
(MDPI, 2021-03) Orihuela, Luis; Millán, Pablo; Rodríguez del Nozal, Álvaro; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
This manuscript deals with the situation in which the different agents or buses in a power network have access to local renewable resources and must manage its use in a distributed fashion. The buses distributedly decide the amount of power to be generated using their local renewable power plants, and that to be demanded from the grid. The decisions are made according to the optimization of a cost function that considers both economic and technical factors. The problem is approached resorting to a game-theoretical framework that requires a negotiation process among the neighboring buses. An iterative algorithm is proposed in order to solve this problem, providing existence and convergence conditions under which the buses reach a suitable equilibrium. The algorithm performance is tested in simulations over a modification of the IEEE 14-bus system, in which the lines are modeled as resistances and distributed generation is considered. Simulations on a network of 44 buses are also included to show the scalability of the method.
A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system
(Institute of Electrical and Electronics Engineers (IEEE), 2019) Gómez-Quiles, Catalina; Asencio Cortés, G.; Gastalver Rubio, Adolfo; Martínez-Álvarez, Francisco; Troncoso Lora, Alicia; Manresa, Joan; Riquelme Santos, José Cristóbal; Riquelme Santos, Jesús Manuel; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.

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