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Increasing thermal comfort and reducing energy consumption are two main objectives of advanced HVAC control systems. In this study, a thermal comfort driven control (PTC-DC) algorithm was developed to improve HVAC control systems with no need of retrofitting HVAC system components. A case building located in Izmir Institute of Technology Campus-Izmir-Turkey was selected to test the developed system. First, wireless sensors were installed to the building and a mobile application was developed to monitor/collect temperature, relative humidity and thermal comfort data of an occupant. Then, the PTC-DC algorithm was developed to meet the highest occupant thermal comfort as well as saving energy. The prototypes of the controller were tested on the case building from July 3rd, 2017 to November 1st, 2018 and compared with a conventional PID controller. The results showed that the developed control algorithm and conventional controller satisfy neutral thermal comfort for 92% and 6% of total measurement days, respectively. From energy consumption point of view, the PTC-DC decreased energy consumption by 13.2% compared to the conventional controller. Consequently, the PTC-DC differs from other works in the literature that the prototype of PTC-DC can be easily deployed in real environments. Moreover, the PTC-DC is low-cost and user-friendly.
Cihan Turhan; Silvio Simani; Gulden Gokcen Akkurt. Development of an energy-efficient personalized thermal comfort driven controller for HVAC systems. Energy 2021, 121568 .
AMA StyleCihan Turhan, Silvio Simani, Gulden Gokcen Akkurt. Development of an energy-efficient personalized thermal comfort driven controller for HVAC systems. Energy. 2021; ():121568.
Chicago/Turabian StyleCihan Turhan; Silvio Simani; Gulden Gokcen Akkurt. 2021. "Development of an energy-efficient personalized thermal comfort driven controller for HVAC systems." Energy , no. : 121568.
In the present work, a control strategy for Maximum Power Point Tracking (MPPT) applied to a wind turbine is described. The electric machine consists of a 1.5MW Doubly Fed Induction Generator (DFIG). This strategy is developed according to the theory of Direct Speed Control (DSC), which includes a state observer. This strategy considers the Low Shaft Speed (LSS) as an input and the iqr reference current as the output. This control mechanism allows monitoring the MPPT; thus, changing the Power Coefficient (Cp) to its optimal value during the wind turbine operation. Among its main features, the controller is configured to work with the incorporation of different wind inputs, a fact that permits evaluating the system’s response to disturbances and variations. For simulation tests, a wind turbine has been modeled in MATLAB-Simulink and Fatigue, Aerodynamics, Structures and Turbulence (FAST) software. The strategy has been compared to a PI-MPPT controller and has demonstrated improvements in terms of speed and output power extraction.
Edy Ayala; Nicolás Pozo; Silvio Simani; Eduardo Muñoz. Direct Speed Control Scheme for Maximum Power Point Tracking of a 1.5MW DFIG Wind Turbine. Proceedings of the Second International Conference on Intelligent Transportation 2021, 918 -928.
AMA StyleEdy Ayala, Nicolás Pozo, Silvio Simani, Eduardo Muñoz. Direct Speed Control Scheme for Maximum Power Point Tracking of a 1.5MW DFIG Wind Turbine. Proceedings of the Second International Conference on Intelligent Transportation. 2021; ():918-928.
Chicago/Turabian StyleEdy Ayala; Nicolás Pozo; Silvio Simani; Eduardo Muñoz. 2021. "Direct Speed Control Scheme for Maximum Power Point Tracking of a 1.5MW DFIG Wind Turbine." Proceedings of the Second International Conference on Intelligent Transportation , no. : 918-928.
The present research proposes a control method applied to a Wind Power Generation Systems (WEGS) for Maximum Power Point Tracking (MPPT) technique based on a Mamdani Fuzzy observer and complemented by a Proportional and Integral (PI) controller using the Direct Speed Control (DSC). This approach allows commanding the rotor side reference current \({\mathrm{i}}_{\mathrm{qr}}\) through the variations of the electromagnetic torque and active electrical power in a Doubly Fed Induction Generator (DFIG) model. Consequently, the speed of the generator is controlled to ensure obtaining a rapid response of the maximum Power Coefficient (\({\mathrm{C}}_{\mathrm{p}}\)). This strategy’s construction starts with direct measurements of the electrical and mechanical variables using computational tools such as FAST and Matlab-Simulink for simulations of a 1.5MW wind turbine model. This DSC strategy presents a rapid performance regarding the tracking of the maximum \({\mathrm{C}}_{\mathrm{p}}\) considering the wind turbine dynamics. This approach contrasts with a traditional PI controller improving the extracted power to analyze the proposed procedure.
Eduardo Muñoz; Edy Ayala; Nicolás Pozo; Silvio Simani. Fuzzy PID Control System Analysis for a Wind Turbine Maximum Power Point Tracking Using FAST and Matlab Simulink. Proceedings of the Second International Conference on Intelligent Transportation 2021, 905 -917.
AMA StyleEduardo Muñoz, Edy Ayala, Nicolás Pozo, Silvio Simani. Fuzzy PID Control System Analysis for a Wind Turbine Maximum Power Point Tracking Using FAST and Matlab Simulink. Proceedings of the Second International Conference on Intelligent Transportation. 2021; ():905-917.
Chicago/Turabian StyleEduardo Muñoz; Edy Ayala; Nicolás Pozo; Silvio Simani. 2021. "Fuzzy PID Control System Analysis for a Wind Turbine Maximum Power Point Tracking Using FAST and Matlab Simulink." Proceedings of the Second International Conference on Intelligent Transportation , no. : 905-917.
Electrical power systems represent a fundamental part of society, and their efficient operations are of vital importance for social and economic development. Power systems have been designed to withstand interruptions under already provided safety and quality principles; however, there are some extreme and not so frequent events that could represent inconveniences for the correct operation of the entire system. For this reason, in recent years the term resilience, which serves to describe the capacity of a system to recover from an unwanted event, has been analyzed on planning, operation and remedial actions. This work is focused on the implementation of a topological reconfiguration tool, which is oriented to change the structure of primary feeders based on changing the status of switchgears. Once the distribution network has been reconfigured, an algorithm of protection coordination is executed based on communication peer-to-peer between Matlab and PowerFactory, which develops an adaptive calculation to determine the current setting and the time multiplier setting. The reconfiguration and coordination protection algorithms could be implemented and evaluated on different distribution networks, areas and locations.
Alex Valenzuela; Silvio Simani; Esteban Inga. Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication. Energies 2021, 14, 3253 .
AMA StyleAlex Valenzuela, Silvio Simani, Esteban Inga. Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication. Energies. 2021; 14 (11):3253.
Chicago/Turabian StyleAlex Valenzuela; Silvio Simani; Esteban Inga. 2021. "Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication." Energies 14, no. 11: 3253.
The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies.
Saverio Farsoni; Silvio Simani; Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. Applied Sciences 2021, 11, 5035 .
AMA StyleSaverio Farsoni, Silvio Simani, Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. Applied Sciences. 2021; 11 (11):5035.
Chicago/Turabian StyleSaverio Farsoni; Silvio Simani; Paolo Castaldi. 2021. "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis." Applied Sciences 11, no. 11: 5035.
The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. In fact, the prompt detection and the reliable diagnosis of faults in their earlier occurrence represent the key point especially for offshore installations. For these plants, operation and maintenance procedures in harsh environments would inevitably increase the cost of the energy production. Therefore, this work investigates fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis strategies that exploit the estimation of the fault by means of data--driven approaches. This solution leads to the development of effective methods allowing the management of partially unknown information of the system dynamics, while coping with measurement errors, the model--reality mismatch and other disturbance effects. In mode detail, the proposed data--driven methodologies exploit fuzzy systems and neural networks in order to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are estimated from the input and output data measurement acquired from the considered dynamic process, the capabilities of their fault diagnosis capabilities are validated by using a high--fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches.
Saverio Farsoni; Silvio Simani; Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. 2021, 1 .
AMA StyleSaverio Farsoni, Silvio Simani, Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. . 2021; ():1.
Chicago/Turabian StyleSaverio Farsoni; Silvio Simani; Paolo Castaldi. 2021. "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis." , no. : 1.
This research proposes a high-performance algorithm for the compression rate of electrical power quality signals, using wavelet transformation. To manage the massive amount of data the telecommunications networks are constantly acquiring it is necessary to study techniques for data compression, which will save bandwidth and reduce costs extensively by avoiding having massive data storage facilities. First biorthogonal wavelet level six transform is applied, however after compression, the reconstructed signal will have a different amplitude and it will be shifted when compared to the original one. Then, normalization is used (for amplitude correction between the original signal and reconstructed one) by multiplying the reconstructed signal by the result of the division between the original signal maximum magnitude and the reconstructed signal maximum magnitude. Thirdly, the ripple in the reconstructed signal is eliminated by applying a moving average filter. Finally, the shifting is corrected by finding the difference between the maximum points in a cycle of the original signal and the reconstructed one. After the compression algorithm was performed the best rates are 99.803% for compression rate, RTE 99.9479%, NMSE 0.000434, and Cross-Correlation 0.999925. Finally, this works presents two new performance criteria, compression time and recovery time, both of them in a real scenario will determinate how fast the algorithm can perform.
Milton Ruiz; Silvio Simani; Esteban Inga; Manuel Jaramillo. A novel algorithm for high compression rates focalized on electrical power quality signals. Heliyon 2021, 7, e06475 .
AMA StyleMilton Ruiz, Silvio Simani, Esteban Inga, Manuel Jaramillo. A novel algorithm for high compression rates focalized on electrical power quality signals. Heliyon. 2021; 7 (3):e06475.
Chicago/Turabian StyleMilton Ruiz; Silvio Simani; Esteban Inga; Manuel Jaramillo. 2021. "A novel algorithm for high compression rates focalized on electrical power quality signals." Heliyon 7, no. 3: e06475.
This paper addresses the development of an active fault tolerant control scheme for avionic systems. The methodology is applied to an aircraft longitudinal autopilot taking into account possible faults on the aircraft actuators. The key feature of the proposed control relies on its active characteristics, as the fault diagnosis strategy is based on a robust estimate of the fault signals that are compensated. The design method uses an intelligent data–driven scheme via a fuzzy modelling and identification procedure, which derives these adaptive filters with disturbance decoupling features. The work shows that these fault estimates can be used for fault accommodation. In particular, the fuzzy approach proposed in the paper provides the reconstruction of the fault signals that are decoupled from the wind components, and thus applied to the aircraft system. The proposed solutions provide interesting robustness features that are analysed by using a high–fidelity simulator, which is able to include different operating points and realistic actuator faults, turbulence, measurement errors, and the model–reality mismatch.
Silvio Simani; Paolo Castaldi; Saverio Farsoni. Fault Diagnosis and Fault-Tolerant Control for Avionic Systems. Advances in Intelligent Systems and Computing 2020, 191 -201.
AMA StyleSilvio Simani, Paolo Castaldi, Saverio Farsoni. Fault Diagnosis and Fault-Tolerant Control for Avionic Systems. Advances in Intelligent Systems and Computing. 2020; ():191-201.
Chicago/Turabian StyleSilvio Simani; Paolo Castaldi; Saverio Farsoni. 2020. "Fault Diagnosis and Fault-Tolerant Control for Avionic Systems." Advances in Intelligent Systems and Computing , no. : 191-201.
The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.
Silvio Simani; Paolo Castaldi. Fault Diagnosis Techniques for a Wind Turbine System. Fault Detection, Diagnosis and Prognosis 2020, 1 .
AMA StyleSilvio Simani, Paolo Castaldi. Fault Diagnosis Techniques for a Wind Turbine System. Fault Detection, Diagnosis and Prognosis. 2020; ():1.
Chicago/Turabian StyleSilvio Simani; Paolo Castaldi. 2020. "Fault Diagnosis Techniques for a Wind Turbine System." Fault Detection, Diagnosis and Prognosis , no. : 1.
This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.
Hamed Habibi; Hamed Rahimi Nohooji; Ian Howard; Silvio Simani. Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation. Energies 2019, 12, 4712 .
AMA StyleHamed Habibi, Hamed Rahimi Nohooji, Ian Howard, Silvio Simani. Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation. Energies. 2019; 12 (24):4712.
Chicago/Turabian StyleHamed Habibi; Hamed Rahimi Nohooji; Ian Howard; Silvio Simani. 2019. "Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation." Energies 12, no. 24: 4712.
This paper suggests a model-free framework for Fault Detection and Isolation (FDI) of satellite reaction wheels for the first time. The proposed FDI method is based on multi-classifier fusion with diverse learning algorithms and configured in a parallel form where a unique module simultaneously performs both detection and isolation tasks. In other words, a multi-classifier-based arrangement is presented on the basis of Mixed Learning strategy where four classic and well-practised classification schemes including Random Forest, Support Vector Machine, Partial Least Square, and Naïve Bayes are incorporated into FDI module in order to make a decision on the occurrence of a fault and its location. Extensive simulation results with a high-fidelity nonlinear spacecraft simulator considering gyroscopic effects, measurement noise, and exogenous aerodynamic disturbance signals show that the proposed FDI scheme can cope with faults affecting reaction wheel torques and obtain promising FDI performances in most of the designed scenarios.
Hasan Abbasi Nozari; Paolo Castaldi; Hamed Dehghan Banadaki; Silvio Simani. Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework. IFAC-PapersOnLine 2019, 52, 194 -199.
AMA StyleHasan Abbasi Nozari, Paolo Castaldi, Hamed Dehghan Banadaki, Silvio Simani. Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework. IFAC-PapersOnLine. 2019; 52 (12):194-199.
Chicago/Turabian StyleHasan Abbasi Nozari; Paolo Castaldi; Hamed Dehghan Banadaki; Silvio Simani. 2019. "Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework." IFAC-PapersOnLine 52, no. 12: 194-199.
Reza Mohammadi Asl; Yashar Shabbouei Hagh; Silvio Simani; Heikki Handroos. Adaptive square-root unscented Kalman filter: An experimental study of hydraulic actuator state estimation. Mechanical Systems and Signal Processing 2019, 132, 670 -691.
AMA StyleReza Mohammadi Asl, Yashar Shabbouei Hagh, Silvio Simani, Heikki Handroos. Adaptive square-root unscented Kalman filter: An experimental study of hydraulic actuator state estimation. Mechanical Systems and Signal Processing. 2019; 132 ():670-691.
Chicago/Turabian StyleReza Mohammadi Asl; Yashar Shabbouei Hagh; Silvio Simani; Heikki Handroos. 2019. "Adaptive square-root unscented Kalman filter: An experimental study of hydraulic actuator state estimation." Mechanical Systems and Signal Processing 132, no. : 670-691.
The exploitation of renewable energies has been increasing, and in particular wind and hydro resources, which have to be effectively transformed into electric power by means of viable technology systems. In order to achieve this point, fuzzy control methods can be considered as simple tools that can be used for this aim. The proposed schemes were already proposed and validated on wind turbine simulators. Therefore, important aspects can be obtained from the application of these control methodologies to hydroelectric processes. This is the key issue of the paper, which tries to give some suggestions for the design and the implementation of these control tools. The working operations of the wind turbine and the hydroelectric plants will be also considered in order to verify the robustness and the reliability aspects of the addressed control methodologies.
Silvio Simani; Stefano Alvisi; Mauro Venturini. Fuzzy Control Techniques for Energy Conversion Systems. Advances in Intelligent Systems and Computing 2019, 943 -955.
AMA StyleSilvio Simani, Stefano Alvisi, Mauro Venturini. Fuzzy Control Techniques for Energy Conversion Systems. Advances in Intelligent Systems and Computing. 2019; ():943-955.
Chicago/Turabian StyleSilvio Simani; Stefano Alvisi; Mauro Venturini. 2019. "Fuzzy Control Techniques for Energy Conversion Systems." Advances in Intelligent Systems and Computing , no. : 943-955.
This paper presents an attitude active fault tolerant control for a satellite simulated model. The proposed fault tolerant controller consists of two main parts: a nominal controller, designed assuming that the system is healthy, and a fault diagnosis module which aims at detecting, isolating and estimating the fault affecting the system. The fault diagnosis module is designed by using the non-linear geometric approach tool which allows to obtain detection residuals and estimation filter decoupled from the aerodynamic disturbance representing the main uncertainty in low Earth orbits. The simulation results show the advantages, in terms of attitude tracking accuracy, obtainable when implementing the proposed method.
Paolo Castaldi; Nicola Mimmo; Silvio Simani. LEO satellite active FTC with aerodynamic disturbance decoupled fault diagnosis. European Journal of Control 2019, 51, 76 -94.
AMA StylePaolo Castaldi, Nicola Mimmo, Silvio Simani. LEO satellite active FTC with aerodynamic disturbance decoupled fault diagnosis. European Journal of Control. 2019; 51 ():76-94.
Chicago/Turabian StylePaolo Castaldi; Nicola Mimmo; Silvio Simani. 2019. "LEO satellite active FTC with aerodynamic disturbance decoupled fault diagnosis." European Journal of Control 51, no. : 76-94.
This paper proposes a three-layer model to find the optimal routing of an underground electrical distribution system, employing the PRIM algorithm as a graph search heuristic. In the algorithm, the first layer handles transformer allocation and medium voltage network routing, the second layer deploys the low voltage network routing and transformer sizing, while the third presents a method to allocate distributed energy resources in an electric distribution system. The proposed algorithm routes an electrical distribution network in a georeferenced area, taking into account the characteristics of the terrain, such as streets or intersections, and scenarios without squared streets. Moreover, the algorithm copes with scalability characteristics, allowing the addition of loads with time. The model analysis discovers that the algorithm reaches a node connectivity of 100%, satisfies the planned distance constraints, and accomplishes the optimal solution of underground routing in a distribution electrical network applied in a georeferenced area. Simulating the electrical distribution network tests that the voltage drop is less than 2% in the farthest node.
Wilson Pavón; Esteban Inga; Silvio Simani. Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration. Sustainability 2019, 11, 1607 .
AMA StyleWilson Pavón, Esteban Inga, Silvio Simani. Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration. Sustainability. 2019; 11 (6):1607.
Chicago/Turabian StyleWilson Pavón; Esteban Inga; Silvio Simani. 2019. "Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration." Sustainability 11, no. 6: 1607.
Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.
Silvio Simani; Paolo Castaldi. Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System. Applied Sciences 2019, 9, 783 .
AMA StyleSilvio Simani, Paolo Castaldi. Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System. Applied Sciences. 2019; 9 (4):783.
Chicago/Turabian StyleSilvio Simani; Paolo Castaldi. 2019. "Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System." Applied Sciences 9, no. 4: 783.
The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes of working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Therefore, the paper aims at providing some guidelines on the design and the application of different data-driven control strategies to a wind turbine benchmark and a hydroelectric simulator. They rely on self-tuning PID, fuzzy logic, adaptive and model predictive control methodologies. Some of the considered methods, such as fuzzy and adaptive controllers, were successfully verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some details of the implementation of the proposed control strategies to these energy conversion systems. The simulations will highlight that the fuzzy regulators are able to provide good tracking capabilities, which are outperformed by adaptive and model predictive control schemes. The working conditions of the considered processes will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many plants.
Silvio Simani; Stefano Alvisi; Mauro Venturini. Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants. Electronics 2019, 8, 237 .
AMA StyleSilvio Simani, Stefano Alvisi, Mauro Venturini. Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants. Electronics. 2019; 8 (2):237.
Chicago/Turabian StyleSilvio Simani; Stefano Alvisi; Mauro Venturini. 2019. "Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants." Electronics 8, no. 2: 237.
This study describes a practical methodology for a resilient planning and routing of power distribution networks considering real scenarios based on georeferenced data. Customers’ demand and their location are the basis for distribution transformer allocation considering the minimal construction costs and reduction of utility’s budget. MST (Minimum Spanning Tree) techniques are implemented to determine the optimal location of distribution transformers and Medium voltage network routing. Additionally, the allocation of tie points is determined to minimise the total load shedding when unusual and extreme events are faced by the distribution grid, improving reliability and resilience reducing downtime during those events. The proposed methodology provides a coverage of 100%, supplying electricity to the totality of customers within statutory limits during normal and unusual conditions.
Alex Valenzuela; Esteban Inga; Silvio Simani. Planning of a Resilient Underground Distribution Network Using Georeferenced Data. Energies 2019, 12, 644 .
AMA StyleAlex Valenzuela, Esteban Inga, Silvio Simani. Planning of a Resilient Underground Distribution Network Using Georeferenced Data. Energies. 2019; 12 (4):644.
Chicago/Turabian StyleAlex Valenzuela; Esteban Inga; Silvio Simani. 2019. "Planning of a Resilient Underground Distribution Network Using Georeferenced Data." Energies 12, no. 4: 644.
The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, data--driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods, such as fuzzy and adaptive self--tuning controllers, were already verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. The working conditions of these systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.
Silvio Simani; Stefano Alvisi; Mauro Venturini. Data–Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants. 2019, 1 .
AMA StyleSilvio Simani, Stefano Alvisi, Mauro Venturini. Data–Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants. . 2019; ():1.
Chicago/Turabian StyleSilvio Simani; Stefano Alvisi; Mauro Venturini. 2019. "Data–Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants." , no. : 1.
The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self-tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will also be taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.
Silvio Simani; Stefano Alvisi; Mauro Venturini. Self-Tuning Control Techniques for Wind Turbine and Hydroelectric Plant Systems. Journal of Power and Energy Engineering 2019, 07, 27 -61.
AMA StyleSilvio Simani, Stefano Alvisi, Mauro Venturini. Self-Tuning Control Techniques for Wind Turbine and Hydroelectric Plant Systems. Journal of Power and Energy Engineering. 2019; 07 (01):27-61.
Chicago/Turabian StyleSilvio Simani; Stefano Alvisi; Mauro Venturini. 2019. "Self-Tuning Control Techniques for Wind Turbine and Hydroelectric Plant Systems." Journal of Power and Energy Engineering 07, no. 01: 27-61.