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This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV's five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles. [ABSTRACT FROM AUTHOR]

The integration of inverter-based generation (IBG), such as solar photovoltaic (PV) and wind systems, presents unique challenges for power grid strength, especially in regions with limited conventional generation capacity. This study investigates the impact of such integration on the strength of Nigeria's power grid, focusing on sparsely connected northern region with abundant renewable resources. Using the network response short-circuit ratio (NRSCR) metric, which accounts for interactions between IBGs, we assess grid strength in three case studies. Simulations were conducted with the Power System Simulation for Engineering (PSS®E) software. Static analysis reveals inherent weakness in the northern region, characterized by low short-circuit capacity. Integrating IBGs at nine strategic locations shows varying responses: strong buses (NRSCR > 10) adapt well, while weak buses (NRSCR < 10), particularly in Kano and Katsina, constrain the operational capacity of the IBGs. Furthermore, close interaction among IBGs at these locations significantly impacts the system's dynamic response, although planned addition of conventional generators offers marginal improvement. This work highlights the potential of renewable resources to address energy shortages but also underscores the crucial need for detailed design studies at each point of interconnection (PoI), considering the longitudinal grid layout in the northern region and potential limitations imposed by IBG interactions. The findings also offer a structured approach that can be applied to power grids with comparable IBG integration levels, similar to the energy landscape of Nigeria.

Abstract The integration of artificial intelligence (AI) models in renewable energy resources management, particularly in the utilization of maximum power point tracking (MPPT) optimizers, has gained significant attention. This study focuses on investigating the tradeoff between accuracy, response time, and system complexity by varying the number of neurons in artificial neural network (ANN) models for MPPT in wind energy conversion systems (WECSs). Traditionally, MPPT algorithms in WECSs are implemented using direct or indirect methods. However, these methods lack an accumulative learning curve and rely on instantaneous inputs. In contrast, ANN models trained on pre-existing datasets offer the potential for improved maximum point capturing processes. Nevertheless, the incorporation of ANN models may introduce additional complexity to the system. Two ANN models, direct and indirect, are examined in comparison to a reference model using the perturb and observe conventional MPPT algorithm. The results show that the ANN direct model exhibits better time response in the face of high variations in wind speed profiles. On the other hand, the ANN indirect model demonstrates a 4% increase in accuracy with minimal ripples.

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV’s five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles.

The importance of renewable energy resources in the energy province necessitates the use of photovoltaic (PV) and wind systems to power brushless DC (BLDC) motors for applications such as water pumping and automobiles. In this research, an optimized MPPT-based positive output super-lift Luo converter (PO-SLLC) is presented for a hybrid power system with a BLDC motor to overcome the constraints of ordinary DC–DC converters. The Luo converter produces a positive output voltage which aids in achieving a high power density with fewer ripples in the voltage and current profile. The output voltage grows in geometric progression, increasing the converter's efficiency, according to its mathematical model. Adjustment parameter enhanced Aquila optimization (APEAO) is used to optimize the duty cycle of the PO-SLLC from the MPPT controller and the controller parameters of the PI controller of the wind system. The proposed system's behavior for variations in irradiation patterns with constant load torque is simulated in MATLAB/Simulink. The settling time and overshoot obtained with the proposed optimized controller are compared to those obtained with other optimization controllers, such as ant colony optimization (ACO), genetic algorithm (GA), Aquila optimization (AO), advanced version of harmony search algorithm (2N + 1-HSA) and opposition based quantum bat algorithm (OQBA) and it is demonstrated that the proposed APEAO optimized controller has good dynamic performance. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) models in renewable energy resources management, particularly in the utilization of maximum power point tracking (MPPT) optimizers, has gained significant attention. This study focuses on investigating the tradeoff between accuracy, response time, and system complexity by varying the number of neurons in artificial neural network (ANN) models for MPPT in wind energy conversion systems (WECSs). Traditionally, MPPT algorithms in WECSs are implemented using direct or indirect methods. However, these methods lack an accumulative learning curve and rely on instantaneous inputs. In contrast, ANN models trained on pre-existing datasets offer the potential for improved maximum point capturing processes. Nevertheless, the incorporation of ANN models may introduce additional complexity to the system. Two ANN models, direct and indirect, are examined in comparison to a reference model using the perturb and observe conventional MPPT algorithm. The results show that the ANN direct model exhibits better time response in the face of high variations in wind speed profiles. On the other hand, the ANN indirect model demonstrates a 4% increase in accuracy with minimal ripples. [ABSTRACT FROM AUTHOR]

This paper focuses on reducing the LCOE (Levelized Cost of Energy), the unmet load, Net Present Cost (NPC) and the greenhouse gas emissions by the utilization of an optimized off-grid integrated renewable energy system. The LCOE has been reduced by 14.8% of that of DG and by 28.6 % of Diesel Generator (DG) with Battery System, whereas the NPC has been reduced by 14.7% of DG only system and by 28.7% of DG with Battery System. A comparison analysis has also been presented based on the performance of the proposed system and most recently published similar studies. To achieve these objectives, an optimization algorithm has been developed and simulated in Hybrid Optimization Model for Multiple Energy Resources (HOMER) software with different combinations of IRES, DG and battery systems. Similarly, intending to analyze the feasibility and performance in terms of the technical and economic aspects of the proposed system, HOMER is used. Based on the obtained results, broadly, it is examined that the proposed system decreases diesel consumption by around 8113 liters/year and the total emissions of greenhouse gas by about 21545 kg/year in the study area; thereby, it can be foreseen as a viable and sustainable option(s) for the development of more such systems in remote locations.

This paper proposed the integration of the wind energy system (WES) with the grid consists of a wind turbine, a permanent magnet synchronous generator (PMSG), a switch-mode rectifier (SMR) and a three-phase voltage source inverter (VSI). Optimal torque control (OTC) method is applied to the converter on the generator side for maximizing the power coefficient under the change in wind speeds. To synchronize the WES with the grid, another controller is applied to keep dc-bus voltage at constant value and to regulate active and reactive power transmitted into a grid. A system of WES connected into a grid is created and tested in the MATLAB/SIMULINK platform version 2019b. Comprehensive simulation results are used to analyze and verify the excellent performance of the suggested two- control strategy on the machine and grid side.

This paper proposes the novel control strategy to improve the power quality injection of wind energy system using Doubly Fed Induction Generator (DFIG) into the grid by implementing artificial neural network. The torque ripple produced in DFIG due to loading by grid tied inverter, which leads to poor power quality injection into the system. Also, these ripples transferred by DC link and causes heating losses and generator phase current distortion. Therefore, this paper modelled ANN based control scheme to reduce the torque ripple content and restrict the transfer of ripple by DC link to improve the outcome of wind energy system while operating in variable conditions. The DFIG system under studied are modelled and simulated in MATLAB SIMULINK to verify the improvement using proposed control strategy. The recent control technique is also simulated for reflecting the effectiveness in the proposed control method. The outcomes obtained are studied and analysed with the existing control scheme to highlight the improvement obtained by proposed control. [ABSTRACT FROM AUTHOR]

In a bid to reduce the negative effect of atmospheric and environmental pollution and its consequent negative effect, a mix of renewable energy generation options is being considered across the globe. Hence, the technical comparison of an ecofriendly, cost-effective, and near zero carbon footprint for sustainable power solutions is still of core interest in the research space. Solar, wind and its hybrids can be further explored to meet the ever-increasing load demand of energy consumers, especially in high energy demand facilities including information technology (IT) services. Critical IT infrastructures represent a core backbone in research institutions, hence the need for hybrid energy solutions is pertinent for increased sustainability. This work compared hybrid energy sources of solar and wind against its independent sources using the IT laboratory in Alex Ekwueme Federal University, Ebonyi, Nigeria. An appropriate sizing of individual and a hybrid of wind, solar, and battery energy source was done, after which the input-output voltages across the set-ups was measured during peak and off-peak periods for a 300W laboratory IT load. The experimental result showed the input/output peak and off-peak DC voltages for solar, wind, and its hybrid energy are 12.67V/12.02V / 12.47V/11.61V, 12.53V / 11.90V / 12.48V / 11.87V, and 12.78V / 12.09V / 12.57 / 11.95V respectively signifying a significant increase from the on-load peak period while the on-load off-peak period is seen to be lower. The results also show that a hybrid solar-wind-battery system tested at 5 minutes time intervals gives a superior output voltage regulation and efficiency when compared to the solar or wind individual systems. It was concluded that the hybrid system of solar, wind, and battery energy source is an ideal standalone renewable energy power option for IT laboratory in the university. [ABSTRACT FROM AUTHOR]

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner. [ABSTRACT FROM AUTHOR]

With abundant and non-polluting benefits in nature, sources of renewable energy have reached vast concentrations. This paper first discusses the number of MPPT (Maximum Power Point Tracking) techniques utilized by wind and photovoltaic (PV) to create hybrid systems for generating wind-PV energy. This hybrid system complements each other day and night to enable continuous power output. Then, a new MPPT technique was proposed to extract maximum power using a newly developed hybrid optimization algorithm, namely the Cukoo Fire Fly method (CFF). The CFF algorithm is derived from the integration of the cuckoo search (CS) algorithm and the Firefly (FF) optimization algorithm. In addition, the proposed system comprises a LUO converter controlled by an integrated proportional controller (PI) which includes control of the power delivered to the load. The LUO converter can increase or decrease power depending on load requirements. The results are compared with Harmony Search Algorithm (HAS), Cuckoo Search (CS) and Firefly (FF). Investigation of the proposed system was carried out on the MATLAB/Simulink platform. [ABSTRACT FROM AUTHOR]

This work investigates the application of sliding mode control (SMC) on a doubly fed induction generator (DFIG). In conventional control schemes like PI controllers, the responses are relatively slow, and the transient state is often subjected to sustained oscillation. Further, the PI control achieves lesser invariance behavior against system uncertainties, and the selection of its gain parameters is a skillful task. In contrast, the SMC is well-known for its faster convergence, robustness, and better transient and steady-state behavior. In this study, the nonsingular fast terminal sliding mode control (NSFTSMC) is applied in the speed loop of the rotor side vector control of DFIG. The proposed NSFTSMC scheme results in less speed fluctuation with a change in wind speed, which is maintained by controlling the torque component of the current (iq*). This paper also presents detailed modeling of the DFIG, power converters, and the related control schemes. Moreover, stability analysis of the proposed methodology ensures the practical finite time stability of the overall system. The comparative controller performance and validation are carried out in Matlab/Simulink environment. The proposed control strategy presents much better results than conventional PI-based control.

• Development of generalized probabilistic model for wind speed intermittency by utilizing real-time acquired cloud-based IoT wind speed data for wind energy system in a SA paradigm. • Simplified reliability analysis through multi-state modeling and probability distribution function for wind-integrated systems, departing from conventional convolution-based approaches. • Forecasting of wind speed and wind power generation for two days in advance through a time-series non-linear autoregressive model with exogenous inputs (NARX), and estimation of the operational reliability using the forecasted wind speed and wind power data. The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations. [ABSTRACT FROM AUTHOR]

Power industry is incidenting a change from the present electric grid to a more secure, reliable, capable and advanced smart grid. Renewable energy sources such as wind and solar energy systems will be incorporated and potentially in bid scale in order to obtain these aims. Many possible applications of renewable energy sources have been inspected. In this article, reliability enhancement of power system considering the impact of wind and solar energy sources integration into grid with advanced intelligent strategies is focused. Further, different models are utilized to obtain the proposed renewable energy sources operation. Further, a reliability assessment framework is discussed to examine the reliability impact of renewable energy sources integration into the grid. A comprehensive case study and risk analysis has been performed based on the different risk indexes to show the usefulness of the proposed strategies and assessment framework, and to give precious insight on the reliability impact resulting from the wind and solar energy resources integration into the grid. The main advantages of renewable energy sources integration into grid with intelligent techniques are presented here along with the reliability evaluation. The results of the study reveals that the integration of the solar and wind energy systems into the grid enhances overall the reliability of the entire power system.

This paper proposed the integration of the wind energy system (WES) with the grid consists of a wind turbine, a permanent magnet synchronous generator (PMSG), a switch-mode rectifier (SMR) and a three-phase voltage source inverter (VSI). Optimal torque control (OTC) method is applied to the converter on the generator side for maximizing the power coefficient under the change in wind speeds. To synchronize the WES with the grid, another controller is applied to keep dc-bus voltage at constant value and to regulate active and reactive power transmitted into a grid. A system of WES connected into a grid is created and tested in the MATLAB/SIMULINK platform version 2019b. Comprehensive simulation results are used to analyze and verify the excellent performance of the suggested two- control strategy on the machine and grid side. [ABSTRACT FROM AUTHOR]

Purpose. Electricity is a basic energy for life and its consumption increased so we need the discovery of new sources of energy such as wind energy .for this ameliorate the quality of generated wind energy by using the intelligent artificial control, this control is made to optimize the performance of three-phase PWM rectifier working. Methodology. These strategies are based on the direct control of the instantaneous power, namely: the control direct power control (DPC) with classic PI regulator and direct power control with fuzzy logic regulator. The fuzzy characterized by its ability to deal with the imprecise, the uncertain has been exploited to construct a fuzzy voltage regulator. The simulation of these methods was implemented using Matlab/Simulink. Results. A comparison with the results obtained by the classic PI showed the improvement in dynamic performance. This makes the fuzzy controller an acceptable choice for systems requiring quick, precise adjustments and less sensitive to outside disturbances. Originality. The proposed this control strategy using for to obtain a performance adjustment of the DC bus voltage and sinusoidal currents on the network side. Practical value. Fuzzy logic is proven to be effective in terms of reducing the harmonic distortion rate of the currents absorbed, correct adjustment of the active and reactive power and DC voltage and unit power factor operation.

The hybrid AC-DC microgrid (HMG) architecture has the merits of both DC and AC coupled structures. Microgrids are subject to intermittence when the renewable sources are used. In the HMG, since power fluctuations occur on both subgrids due to varying load and unpredictable power generation from renewable sources, proper voltage and frequency regulation is the critical issue. This article proposes a unique method for operating a microgrid (MG) comprising of PV array, wind energy system (WES), fuel cell (FC), and battery in HMG configuration. The control scheme of the interlinking converter (ILC) regulates frequency, voltage, and power flow amongst the subgrids. Power management in the HMG is investigated under different scenarios. Proper power management is accomplished within the individual subgrids and among the subgrids by the control techniques adopted in the HMG. The system voltage and frequency deviations are found to be minimized when the FC system acts as the backup source for DC subgrid, reducing the power flow through the ILC.

Abstract This paper presents a novel composite wide area control of a DFIG wind energy system which combines the Robust Exact Differentiator (RED) and Discontinuous Integral (DI) control to damp out inter-area oscillations. RED generates the real-time differentiation of a relative speed signal in a noisy environment while DI control, an extension to a twisting algorithm and PID control, develops a continuous control signal and hence reduces chattering. The proposed control is robust to disturbances and can enhance the overall stability of the system. The proposed composite sliding mode control is evaluated using a modified benchmark two-area power system model with wind energy integration. Simulation results under various operating scenarios show the efficacy of the proposed approach.

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner.

Many studies have been made in terms of mechanical and electrical related to using of convert the wind energy into electrical energy. The mechanical studies are generally wind turbine and special type machine designs. The electrical studies are the studies on power converter circuits, grid integration, and maximum power point tracking. A wind energy conversion system (WECS) is required to perform electrical studies. However, if there is no possibility to study in a wind power plant, the real-time model of the wind energy system should be established in the laboratory environment. The WECSs can be in many different topologies. Generally; the WECSs consist of wind turbine/wind turbine emulator, generator and power converter units. Although much work has been done in the literature on this topic, it is not possible to obtain detailed information about the established real-time system model. In this study, considering this aim, the real-time wind energy system is presented in detail and it is aimed to shed light on the researchers working in this field. The graph of the performance coefficient required for maximum power point tracking in the established experimental setup has been obtained and it was shown that the established experimental setup is similar to the real wind energy system. In addition, the system has been operated at maximum power point by applying the designed control algorithm to the system for a constant wind speed.

The hybrid AC-DC microgrid (HMG) architecture has the merits of both DC and AC coupled structures. Microgrids are subject to intermittence when the renewable sources are used. In the HMG, since power fluctuations occur on both subgrids due to varying load and unpredictable power generation from renewable sources, proper voltage and frequency regulation is the critical issue. This article proposes a unique method for operating a microgrid (MG) comprising of PV array, wind energy system (WES), fuel cell (FC), and battery in HMG configuration. The control scheme of the interlinking converter (ILC) regulates frequency, voltage, and power flow amongst the subgrids. Power management in the HMG is investigated under different scenarios. Proper power management is accomplished within the individual subgrids and among the subgrids by the control techniques adopted in the HMG. The system voltage and frequency deviations are found to be minimized when the FC system acts as the backup source for DC subgrid, reducing the power flow through the ILC. [ABSTRACT FROM AUTHOR]

This paper presents a novel composite wide area control of a DFIG wind energy system which combines the Robust Exact Differentiator (RED) and Discontinuous Integral (DI) control to damp out inter-area oscillations. RED generates the real-time differentiation of a relative speed signal in a noisy environment while DI control, an extension to a twisting algorithm and PID control, develops a continuous control signal and hence reduces chattering. The proposed control is robust to disturbances and can enhance the overall stability of the system. The proposed composite sliding mode control is evaluated using a modified benchmark two-area power system model with wind energy integration. Simulation results under various operating scenarios show the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Purpose. Electricity is a basic energy for life and its consumption increased so we need the discovery of new sources of energy such as wind energy .for this ameliorate the quality of generated wind energy by using the intelligent artificial control, this control is made to optimize the performance of three-phase PWM rectifier working. Methodology. These strategies are based on the direct control of the instantaneous power, namely: the control direct power control (DPC) with classic PI regulator and direct power control with fuzzy logic regulator. The fuzzy characterized by its ability to deal with the imprecise, the uncertain has been exploited to construct a fuzzy voltage regulator. The simulation of these methods was implemented using Matlab/Simulink. Results. A comparison with the results obtained by the classic PI showed the improvement in dynamic performance. This makes the fuzzy controller an acceptable choice for systems requiring quick, precise adjustments and less sensitive to outside disturbances. Originality. The proposed this control strategy using for to obtain a performance adjustment of the DC bus voltage and sinusoidal currents on the network side. Practical value. Fuzzy logic is proven to be effective in terms of reducing the harmonic distortion rate of the currents absorbed, correct adjustment of the active and reactive power and DC voltage and unit power factor operation. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive survey on the reliability evaluation of the electrical network system. The impacts of integration of new and renewable energy sources (electric vehicle, energy storage system, solar, and wind) on the reliability of electrical power system (EPS) are discussed. The impacts of these renewable sources have merits/demerits when these sources are integrated with the conventional electric power system. However, the merits are predominant as it includes unlimited, free, and cost‐effective resources. The recent researches depict that the uncertainties of renewable energy resources leads to the probabilistic and reliability analyses of EPS. EPS includes offshore and onshore wind farms, micro‐grid, energy storage system, and other high voltage grids. It also contains the failure‐prone components related to the power systems. For the accomplishment of these aspects, the handling methods of uncertainty parameters in generation, transmission, and distribution systems are discussed. The incorporation of electric vehicles, wind energy system, and energy storage system for reliability assessment is also discussed briefly. This study also presents the scope of a new research area for the researchers on the reliability assessment of renewable energy integrated power system. [ABSTRACT FROM AUTHOR]

The current paper introduces a realistic solution for energy demand in Makadi Bay, Red-Sea, Hurgada, Egypt using energy system crossbred of Renewable Wind Energy System (WES) and Photovoltaic System (PVS) in the presence of Battery Energy Storage (BES). A real measurement for wind speed was recorded through a year of 2017. Also, the sun irradiance and temperature were recorded through the same period, to be considered for the output power calculations from the proposed crossbred renewable energy system. The demand load data for the city was recoded as well as through the same period for evaluating the feasibility of the system if it can cover the city loads. Linear TORSCHE optimization technique has utilized to reach an optimum solution of the proposed crossbred renewable energy system. Individual configuration of PVS & WES in presence of BES have been studied and compared with the hybrid PV/WT. Furthermore, economic analysis has presented to prove the best economical system. The obtained results show that installing such hybrid system consists of WES, PVS and BES is cheaper than installing each one individually. [ABSTRACT FROM AUTHOR]

This study analyses the dynamic behaviour of a doubly fed induction generator (DFIG)‐based wind integrated power system (WIPS) resulting from a major disturbance. The effects of transient disturbance on WIPS are examined without and with a controller in terms of performance and stability of the system. To enhance the performance of WIPS after a sudden disturbance, an optimally designed linear quadratic regulator (LQR) controller is applied to the system. The wind energy system is described in state‐space representation, whose states and outputs are taken as feedback to the controller for improving their dynamic response. An artificial bee colony (ABC)‐based swarm optimisation technique is used to evaluate the optimal weighting matrices of the LQR controller in parallel with minimising the performance index and dynamic response characteristics of the system. The effectiveness of the proposed ABC–LQR controller in WIPS is verified by comparing their simulation and numerical results with standard proportional–integral and LQR controllers. It indicates that the proposed controller provides better enhancement of dynamic response as compared with other controllers in terms of performance index and dynamic response characteristics. Moreover, the robustness and stability of various system configurations are tested by the eigenvalue analysis. [ABSTRACT FROM AUTHOR]

Wind energy conversion system (WECS) is bit by bit gaining interest as a suitable source of renewable energy. In this paper a direct AC-AC matrix converter is utilized to interface an isolated static load fed from WECS. A self-excited induction generator is utilized as it has many advantages in terms of ease of maintenance, self-protection against short circuits. This paper proposes a modified open loop control of matrix converter with indirect space vector modulation. The modified open loop provides constant output voltage and constant frequency even if the wind speed changed. Matrix converter (MC) is used rather than AC-DC-AC converter as it eliminates the bulky capacitors. Finally, simulation results for different operating points are displayed which are consistent with the expected results.

The replacement of a conventional synchronous generator (SG) with higher wind penetration greatly reduces the inertial support available for the system. Therefore, a new consideration – the dynamic frequency changes in a wind energy system using a super‐capacitor (SC) – is proposed here to provide faster, limitless, inertial response during load disturbances. By considering the dynamic frequency change in defining the power exchange between SC and network, this proposed method not only provides inertial and primary frequency response but is also able to avoid a second frequency dip. To avoid deep charge and discharge of SC, a simple energy management system (EMS) which considers the system frequency deviation and the state of charge (SOC) of SC is adopted in this technique. The effectiveness of the proposed technique is modelled in MATLAB/Simulink software. Comparative analyses between the proposed virtual inertial support (VIS)‐based SC and existing VIS control strategies are performed. The simulation results obtained prove that the proposed VIS greatly reduces the frequency nadir and peak frequency of the system during sudden load increase and decrease, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes the decoupling control of direct torque control (DTC) and vector control (VC) for doubly fed induction generator (DFIG)-based wind energy system (WES). DTC controls the generator speed, torque and rotor flux; similarly VC regulates the capacitor link voltage and power quality of grid. Furthermore, proposed technique analyzed with sliding mode control (SMC) and conventional controllers independently. SMC shows better improvements of both stator and rotor currents, capacitor link voltage, torque and generator speed in contrast to proportional and integral controller. The proposed DTC-VC aims to improve performance of transient and dynamic behavior of DFIG-based WES during sine nature of torque and random as well as step change variation of generator speed. It is also tested in real-time environment at 7.5 hp as well as MATLAB/Simulink of 5.5 kw rating. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a Neural Input–Output Feedback Linearization (N-IOFL) controller for a Doubly Fed Induction Generator (DFIG) prototype connected to the grid. Under unbalanced grid voltages, the existing control strategies need to be modified, becoming very complicated. By using an identifier based on Recurrent High Order Neural Network (RHONN), trained on-line with an Extended Kalman Filter (EKF), an adequate model of the DFIG and of the DC-link can be obtained, which helps the control law based on feedback linearization to reject grid disturbances appearing under non-ideal grid conditions without decomposition process. Based on such identification, the proposed controller is used to track a desired Direct Current (DC) voltage reference at the output of DC-link, to maintain constant the electric power factor controlled by the Grid Side Converter (GSC), and to force independently the rotor currents to track a specified reference defined from the required stator powers, controlled by the Rotor Side Converter (RSC), under both balanced and unbalanced grid conditions. Real-time results illustrate the effectiveness of the proposed controller even in presence of non-ideal grid conditions. Highlights • Investigating the Doubly Fed Induction Generator connected with the grid in presence of grid disturbances. • Implementing an identifier based on Recurrent High Order Neural Networks. • Using Extended Kalman filter to train online the proposed neural identifier. • Linearizing the controlled system using the neural Input–Output Feedback Linearization control. • Implementing in real-time the proposed control scheme and testing it under grid disturbances. [ABSTRACT FROM AUTHOR]

This article presents a modelling and sliding mode control (SMC) of a doubly fed induction generator (DFIG) integrated into wind energy system for independent control of stator reactive and stator active powers. For a comparative study, the independent control of reactive and active powers is ensured in the first step by neuro-sliding mode controllers (NSMC) and the second step by neuro-second order sliding mode controllers (NSOSMC). Finally, the performance of the system is tested and compared by simulation in terms of robustness tests, and reference tracking tests based on Matlab/Simulink software. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic modeling and control of doubly fed induction-generator (DFIG) based on the wind turbine systems. Active and reactive power control of the DFIG are based on the feedback technique by using the suitable voltage vectors on the rotor side. The rotor flux has no impact on the changes of the stator active and reactive power. The proposed controller is based on the feedback technique in order to reduce the oscillation of the generator. The control approach is estimated through the simulation result of the feedback controller assembled with DFIG wind turbines. It is applied by the feedback control based techniques in order to control the power flowing of DFIG and the power grid. Hence, an improved feedback control technique is adopted to get a better power flow transfer and to improve the dynamic system and transient stability. In stable condition, the improved performance of the controller, the proposed method is verified for the effectiveness of the control method is done in stable conditions.

In this paper, a systematic synchronization procedure is proposed for a doubly fed induction generator (DFIG) during unbalanced grid voltage conditions. The initial induced voltage at the open stator terminal is required to synchronize with the grid voltage in magnitude, frequency and phase. An open stator negative sequence rotor current controller is implemented with the conventional DFIG vector controller, which allows the induced stator voltage to become as unbalanced as the grid voltage, hence enabling a smooth connection. A brief comparison is provided for practical issues such as controller structure variation between DFIG open stator and normal operating conditions, and initial encoder rotor angle measurement offset. The procedure is validated experimentally on a 2.2 kW laboratory-scaled DFIG test bench.

This paper proposes a solution to exploit the wind potential of the Danube cluster by storing it in the Iron Gates I reservoir lake, through pumping the water downstream of the hydroelectric power plant. Storing energy by pumping water, i.e. turning the plant into a pumped storage hydroelectric plant, will chancel the unstable character of wind energy, also achieving peak load coverage under conditions of investment much be-lower to those of building a hydroelectric power plant with accumulation by pumping the Danube. [ABSTRACT FROM AUTHOR]

Stabilization of wind farm (WF) is considered one of the major serious issues for developing power systems based on renewable energy sources. A comparison of dynamic performances for FACTS devices like STATCOM and SVC is presented in this paper. Such devices are employed to stabilize WF integrated with multi-machine power system (WFMPS). Fuzzy logic controller (FLC) has been employed for controlling FACTS devices to enhance WFMPS performance under different abnormal conditions. Three-phase to ground short circuit fault has been taken into consideration in different locations of system. MATLAB/Simulink is used to perform the modeling and simulation of WFMPS. The obtained results demonstrate the effect of both SVC- and STATCOM-based FLC on damping the system oscillations and improving the system dynamic performance in the post-fault duration. The comparison confirms superior dynamic performance and fast fault recovery of STATCOM-based FLC through different fault conditions compared with SVC-based FLC. [ABSTRACT FROM AUTHOR]

This paper presents the effectiveness of the interior search algorithm in economic power scheduling of a grid-tied DC microgrid with renewable generation (wind and photovoltaic) and battery energy storage. The study presents the modelling and simulation of various DC/DC converters for tracking maximum power from wind and photovoltaic sources and the bidirectional power flow of battery energy storage. The DC microgrid and its controllers were modelled and simulated in MATLAB/Simulink. The generating units were dispatched economically using the interior search algorithm with the objective to minimize the operating cost of the microgrid. The simulated results verify the effectiveness of the interior search algorithm as the daily cost of microgrid operation was reduced by 11.25%.

The incorporation of renewable energy resources (RERs) into electrical grid is very challenging problem due to their intermittent nature. This paper solves an optimal power flow (OPF) considering wind-solar-storage hybrid generation system. The primary components of the hybrid power system include conventional thermal generators, wind farms and solar photovoltaic modules with batteries. The main critical problem in operating the wind farm or solar PV plant is that these RERs cannot be scheduled in the same manner as conventional generators, because they involve climate factors such as wind velocity and solar irradiation. This paper proposes a new strategy for the optimal power flow problem taking into account the impact of uncertainties in wind, solar PV and load forecasts. The simulation results for IEEE 30 bus system with genetic algorithm (GA) and two-point estimate method have been obtained to test the effectiveness of the proposed optimal power flow strategy. Results for a sample system with GA and two-point estimate OPF, and GA and Monte Carlo simulation have been obtained to ascertain effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the control scheme of a distributed high-speed generator system with a total amount of 12 generators and nominal generator speed of 7000 min −1 is studied. Specifically, a fault tolerant control (FTC) scheme is proposed to keep the turbine in operation in the presence of up to four simultaneous generator faults. The proposed controller structure consists of two layers: The upper layer is the baseline controller, which is separated into a partial load region with the generator torque as an actuating signal and the full-load operation region with the collective pitch angle as the other actuating signal. In addition, the lower layer is responsible for the fault diagnosis and FTC characteristics of the distributed generator drive train. The fault reconstruction and fault tolerant control strategy are tested in simulations with several actuator faults of different types. [ABSTRACT FROM AUTHOR]

In this paper, the active front-end (AFE) converter topology for the total harmonic distortion (THD) reduction in a wind energy system (WES) is used. A higher THD results in serious pulsations in the wind turbine (WT) output power and several power losses at the WES. The AFE converter topology improves the capability, efficiency, and reliability in the energy conversion devices; by modifying a conventional back-to-back converter, from using a single voltage source converter (VSC) to use pVSC connected in parallel, the AFE converter is generated. The THD reduction is achieved by applying a different phase shift angle at the carrier of digital sinusoidal pulse width modulation (DSPWM) switching signals of each VSC. To verify the functionality of the proposed methodology, the WES simulation in Matlab-Simulink® (Matlab r2015b, Mathworks, Natick, MA, USA) is analyzed, and the experimental laboratory tests using the concept of rapid control prototyping (RCP) and the real-time simulator Opal-RT Technologies® (Montreal, QC, Canada) is achieved. The obtained results show a type-4 WT with a total output power of 6 MVA, generating a THD reduction up to 5.5 times of the total WES current output by Fourier series expansion.

In this paper, an adaptive sliding mode speed control algorithm with an integral-operation sliding surface is proposed for a variable speed wind energy experimental system. In the control design, an estimator is designed to compensate for the uncertainties and the unknown turbine torque. In addition, the bound of the sliding mode is investigated to deal with uncertainties. The stability of the system can be guaranteed in the sense of the Lyapunov stability theorem. The laboratory size DC generator wind energy system is controlled using a buck-boost DC-DC converter interface. The control system is validated by experimentation and results demonstrate the achievement of favorable speed tracking performance and robustness against parametric variations and external disturbances.

Recent developments and trends in the electric power consumption indicate an increasing use of renewable energy. Renewable energy technologies offer the promise of clean, abundant energy gathered from self-renewing resources such as the sun, wind, earth and plants. Virtually all regions of the world have renewable resources of one type or another. By this point of view studies on renewable energies focuses more and more attention. The present paper intends to present different mathematical models related to different types of renewable energy sources such as: solar energy and wind energy. It is also presented the validation and adaptation of such models to hybrid systems working in geographical and meteorological conditions specific to central part of Transylvania region. The conclusions based on validation of such models are also shown.

Power management in a grid-integrated power system consisting of distributed renewable energy sources is necessitated by ever-increasing energy consumption, the high cost of production, the limited resource of fossil fuel, and the worsening global environment. Wind and solar power generation are two of the most promising renewable power generation technologies. The growth of wind and photovoltaic (PV) power generation systems has exceeded the most optimistic levels of expectations. Fuel cells (FCs) also have great potential to be green power sources of the future because of the many merits they have, such as high efficiency and zero or low emission of pollutant gases. In this paper the modeling, control, and analysis of grid-connected PV power, a proton exchange membrane fuel cell (PEMFC), and a wind energy system (WES) connected through a common DC bus linked to an AC grid is discussed. In coordination with the PEMFC, the hybrid system's output power becomes controllable so that full utilization of PV and wind power takes place based on its availability, and the DC bus voltage is maintained constant even at varying load conditions for the desired power sharing between the distributed generation (DG) sources, load, and the AC grid. The hybrid system is controlled through a combination of two proposed control modes, namely the DG control mode and grid control mode. The coordination of the power controller, voltage source converter controller, and load controller is such that the transition between the two control modes is done as smoothly as possible. Thus, the total DG power will be a combination of PV power, WES power, and power produced by the PEMFC. Any additional power if required up to the maximum loading will be supplied by grid. If the load exceeds its maximum value, then the load controller will carry out load shedding. The composite system is available for safe operation in on-grid as well as off-grid mode satisfying voltage and power balance constraints. Different modes of operation have been demonstrated to verify and validate the desired power management among the DG sources, load, and the grid. MATLAB/Simulink software was used for simulating the composite model. [ABSTRACT FROM AUTHOR]

In this paper, the control scheme of a distributed high-speed generator system with a total amount of 12 generators and nominal generator speed of 7000 min − 1 is studied. Specifically, a fault tolerant control (FTC) scheme is proposed to keep the turbine in operation in the presence of up to four simultaneous generator faults. The proposed controller structure consists of two layers: The upper layer is the baseline controller, which is separated into a partial load region with the generator torque as an actuating signal and the full-load operation region with the collective pitch angle as the other actuating signal. In addition, the lower layer is responsible for the fault diagnosis and FTC characteristics of the distributed generator drive train. The fault reconstruction and fault tolerant control strategy are tested in simulations with several actuator faults of different types.

• A new data-driven model for the wind power probabilistic prediction at any resolution in a horizon is developed. • An attention-assisted ordinary differential equation network based modeling method is proposed. • A new two-stage training process is introduced for the proposed data-driven modeling process. • Developed model predicts quantiles of the future wind power with addressing quantile-crossing issues. To improve the practicality, data-driven techniques of predicting the wind power generation and its uncertainty still need to address three technical challenges, uplifting the prediction accuracy via inventing an emerging data analytics mechanism, flexibly scaling up the prediction resolution from the data sampling resolution, and preventing invalid probabilistic prediction results. This study is thus motivated to investigate an advanced prediction method enabling highly accurate and valid probabilistic wind power predictions as well as the capability of a resolution scale-up. The long short term memory (LSTM) network combined with an attention-assisted ordinary differential equation network (AODEN), LSTM-AODEN, is developed for the first time in the literature to produce a novel deep network architecture for probabilistic wind power predictions via leveraging advantages of deep learning and ordinary differential equations. In the LSTM-AODEN, a two-stage training scheme, which sequentially develops one median prediction model and one multi-interval length prediction model, is proposed to fully eliminate quantile crossings and guarantee the validity of prediction results. Six evaluation metrics in computational experiments verify that the proposed LSTM-AODEN method leads to overall highly accurate and fully valid results of the point prediction, interval prediction, and quantile prediction compared to several classes of state-of-the-art probabilistic prediction methods. Meanwhile, the proposed method is proved to offer a unique capability of generating higher-resolution probabilistic wind power prediction results, which is gained from the AODEN, indicated by the lowest prediction errors. [ABSTRACT FROM AUTHOR]

With the rapid growth of the wind energy systems in the past years and their interconnection with the existing power system networks, it has become very significant to analyse and enhance the transient stability of the wind energy conversion systems connected to the grid. This study investigates the transient stability enhancement of a grid‐connected wind farm using doubly‐fed induction machine‐based flywheel energy storage system. A cascaded adaptive neuro‐fuzzy controller (ANFC) is introduced to control the insulated gate bipolar transistor switches‐based frequency converter to enhance the transient stability of the grid‐connected wind farm. The performance of the proposed control strategy is analysed under a severe symmetrical fault condition on both a single‐machine infinite bus model and the IEEE‐39 bus New England test system. The transient performance of the system is investigated by comparing the results of the system using the proposed ANFCs with that of the black‐box optimisation technique‐based proportional–integral controllers. The validity of the system is verified by the simulation results which are carried out using PSCAD/EMTDC environment. [ABSTRACT FROM AUTHOR]