Wind Turbine Engine Research Articles

Decentralized Robust Control of a Coupled Wind Turbine and Diesel Engine Generator System

This paper presents the design of a robust decentralized controller for the frequency and active power control of a single stand-alone (off-grid) mechanically-coupled generator system including wind turbine and diesel engine generator subsystems. Two robust decentralized controllers are designed and optimized for the two subsystems by using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> -synthesis technique. The decentralized controllers address the mechanical coupling between the two subsystems without communicating with each other. In addition, both controllers are designed in such a way that they are robust against the concurrent variations of all the system parameters, which in turn improves the frequency and power control performances significantly. Simulation results indicate that the proposed decentralized controllers have a better performance as compared to the benchmark decentralized model predictive controllers and decentralized linear-quadratic Gaussian controllers. It is also demonstrated that the proposed control scheme is able to damp out mild and large disturbances originating from the load and wind power changes, and has an acceptable robust performance against the concurrent variations in all parameters of the system.

Decentralized Model-Predictive Control of a Coupled Wind Turbine and Diesel Engine Generator System

It is highly critical that renewable energy-based power generation units provide continuous and high-quality electricity. This requirement is even more pronounced in standalone wind–diesel systems where the wind power is not always constant or available. Moreover, it is desired that the extracted power be maximized in such a way that less fuel is consumed from the diesel engine. This paper proposes a novel method to design decentralized model-predictive controllers to control the frequency and power of a single standalone generation system, which consists of a wind turbine subsystem mechanically coupled with a diesel engine generator subsystem. Two decentralized model-predictive controllers are designed to regulate the frequency and active power, while the mechanical coupling between the two subsystems is considered, and no communication links exist between the two controllers. Simulation results show that the proposed decentralized controllers outperform the benchmark decentralized linear-quadratic Gaussian (LQG) controllers in terms of eliminating the disturbances from the wind and load power changes. Furthermore, it is demonstrated that the proposed control strategy has an acceptable robust performance against the concurrent variations in all parameters of the system as compared to the LQG controllers.

Performance analysis of multistage PID controller for frequency regulation of multi microgrid system using atom search optimisation

Due to the presence of unpredictable renewable energy sources, the frequency control problem is a complex task in multi microgrid (MMG) systems. This research work recommends an atom search optimisation (ASO)-based multistage proportional–integral–derivative (M-PID) controller for frequency control of the MMG system under random and stochastic environments. The studied MMG system is a combination of photovoltaic, wind turbine and diesel engine generators along with diverse energy storage devices such as battery energy storage system and flywheel energy storage system. A comparative study with genetic algorithm and particle swarm optimisation is done to validate the effectiveness of the proposed technique. This prospective analysis is validated in OPAL-RT to check the practicability of this research.

Enhanced tribological properties of wind turbine engine oil formulated with flower-shaped MoS2 nano-additives

Wind energy has become one of the most viable green and sustainable energy sources. A major concern of this sector is the damage of mechanical components and energy losses caused by repeated friction and wear, which lead to reduced efficiency and increased maintenance costs. The development of new lubricant additives with high anti-wear properties and friction reduction capabilities is of paramount importance to ensure long service life and high efficiency of mechanical devices. Herein, flower-shaped MoS2 nano-additives of about 150 nm were prepared under mild conditions through a hydrothermal process. Octyl- and octadecyltrichlorosilane were used as surface hydrophobization agents to enhance the colloidal stability of MoS2 nano-additives formulations in wind turbine engine oil. Increasing the length of the alkyl chain grafted onto the particles improved the colloidal stability up to at least one year and, more importantly, resulted in the lowest friction and best wear reduction capabilities, as shown by the pin-on-disk tribometer and laboratory wear tests.

Improving frequency regulation of wind‐integrated multi‐area systems using LFA‐fuzzy PID control

Due to the complexity and importance of the electricity grid stability, sophisticated control systems and computational approaches are essentially required to solve dynamical system frequency problems in multi-area systems. Participation of wind turbines in power system frequency control in the form of stepwise inertial control in the presence of a proposed adequately tuned fuzzy-PID load-frequency control is investigated in this paper. A two-area power system is studied in which it is linked through tie-line and equipped with fuzzy-PID control systems. To increase the energy transition and flexibility of the entire system against frequency events and parameters uncertainties, the fuzzy-PID controllers are first tuned using lightning flash algorithm (LFA). The tuned controllers are then used in each area to optimally counterbalance the frequency deviations and the tie-line power of the interconnected areas against varying load disturbances, wind power fluctuations, and to keep the renewable integrated system in a stable state. The sensitivity analysis is also conducted with a wide range of system parameter variations, uncertainties, and disturbances. Furthermore, the LFA-tuned fuzzy-PID load-frequency control is also tested in a two-area isolated microgrid, comprising wind turbine, PV panels, fuel cells, micro-turbine, and diesel engine generator. The results obtained using the proposed approach are compared with other state-of-the-arts algorithms and control systems in the literature. The compared results show the efficiency and robustness of the proposed optimization-based controller for better frequency control in multi-area power systems and isolated microgrids. The results also demonstrate that a finely tuned automatic generation controller gives a significant boost to grid frequency control when wind turbines participate in the inertial control, suggesting migration from conventional PID-based automatic generation control to more adaptive control approaches.

Optimized Robust Controller Design Based on CPSOGSA Optimization Algorithm andH2/H∞Weights Distribution Method for Load Frequency Control of Micro-Grid

In this paper, a mixed H2/H∞ robust control strategy that considers the weights of the H2 and H∞ norm in the optimization process is proposed, and it is used to solve the load frequency control (LFC) problem of the micro-grid (MG). The MG load frequency model established in this paper includes battery energy storage system (BESS), fuel cell (FC), wind turbine (WT), photo-voltaic (PV), and diesel engine generator (DEG). The optimal mixed H2/H∞ robust controller takes the minimum square integral of the system’s frequency fluctuation as the goal of control optimization by integrating the robust performance expressed by the H2/H∞ two norms. The hybrid particle swarm optimization and gravitational search algorithm with chaotic map algorithm (CPSOGSA) is used to optimize the weight value reflecting the H2 and H∞ performance of the system and the evaluation function’s weighting matrix of the output performance so that the controller can reach the optimum under the constraints. Simulation experiments show that the robust controller designed by the proposed method has better dynamic performance when compared with H∞ robust controller, H2 robust controller, and traditional H2/H∞ robust controller, and the results are very satisfactory.

Research on improvement of ambient temperature of wind turbine engine room

A good cabin environment and reasonable cabin temperature are a powerful guarantee for the safe operation of wind turbines. By installing the cabin intelligent intelligent filtering and cooling system in the wind turbine, the cabin environment can be effectively improved, the cabin temperature can be reduced, the downtime of the wind turbine can be reduced, the grid-connected operation time of the wind turbine can be improved, the safe operation of the wind farm can be guaranteed, and the wind farm can be improved. Operational efficiency and economic benefits.

Design and Analysis of 2dof-PID Controller for Frequency Regulation of Multi-Microgrid Using Hybrid Dragonfly and Pattern Search Algorithm

This research work proposes a hybrid dragonfly algorithm and pattern search (hDA-PS) technique to optimize the parameters of two-degree-of-freedom proportional integral derivative controller (2dof-PID) for load frequency control of multi-microgrid (MG) system. The multi-MG contains power pool of photovoltaic (PV), wind turbine (WTG) and diesel engine generator along with different energy storage devices like battery energy storage system and flywheel energy storage system. The frequency control problem under the influence of stochastic disturbance condition is formulated as an optimization problem, and hDA-PS is implemented to search for the optimal controller parameters. To demonstrate the supremacy of the proposed 2dof-PID controller, the performances of 2dof-PID controller are compared with conventional PID and PI controllers. A comparative study with particle swarm optimization, genetic algorithm and dragonfly algorithm is done to justify supremacy of hDA-PS algorithm.

A Computational Architecture for Coupling Heterogeneous Numerical Models and Computing Coupled Derivatives

One of the challenges in computational modeling is coupling models to solve multidisciplinary problems. Flow-based computational frameworks alleviate part of the challenge through a modular approach, where data flows from component to component. However, existing flow-based frameworks are inefficient when coupled derivatives are needed for optimization. To address this, we develop the modular analysis and unified derivatives (MAUD) architecture. MAUD formulates the multidisciplinary model as a nonlinear system of equations, which leads to a linear equation that unifies all methods for computing derivatives. This enables flow-based frameworks that use the MAUD architecture to provide a common interface for the chain rule, adjoint method, coupled adjoint method, and hybrid methods; MAUD automatically uses the appropriate method for the problem. A hierarchical, matrix-free approach enables modern solution techniques such as Newton--Krylov solvers to be used within this monolithic formulation without computational overhead. Two demonstration problems are solved using a Python implementation of MAUD: a nanosatellite optimization with more than 2 million unknowns and 25,000 design variables, and an aircraft optimization involving over 6,000 design variables and 23,000 constraints. MAUD is now implemented in the open source framework OpenMDAO, which has been used to solve aircraft, satellite, wind turbine, and turbofan engine design problems.

HOMER analysis of the water and renewable energy nexus for water-stressed urban areas in Sub-Saharan Africa

Climate change, population growth and rapidly increasing urbanisation severely threaten water quantity and quality in Sub-Saharan Africa. Treating wastewater is necessary to preserve the water bodies; reusing treated wastewater appears a viable option that could help to address future water challenges. In areas already suffering energy poverty, the main barrier to wastewater treatment is the high electricity demand of most facilities. This work aims to assess the benefits of integrating renewable energy technologies to satisfy the energy needs of a wastewater treatment facility based on a conventional activated sludge system, and also considers the case of including a membrane bioreactor so treated wastewater can be reused for irrigation. Using HOMER, a software tool specifically developed for optimal analysis of hybrid micro-generation systems, we identify the optimal combination of renewable energy technologies for these facilities when located in a specific water-stressed area of Sub-Saharan Africa and assess whether the solutions are cost-effective. The analysis shows investment in renewable technologies is cost-effective when the true cost of electricity or average days of power outages per year are considered. Integration of photovoltaic panels, a wind turbine and internal combustion engine fuelled by biogas produced by anaerobic digestion can cover between 33% and 55% of the electricity demand of the basic wastewater facility, at a levelised cost of energy lower than the true cost of electricity. In the case of water reuse, the techno-economically viable solutions identified by HOMER can cover 13% of energy needs. Finally, we discuss how the proposed solutions could provide a large contribution to socio-political security, in both domestic and cross-border contexts.

Performance enhancement of hybrid AC/DC microgrid based D-FACTS

This paper introduces a novel hybrid AC/DC microgrid design based on distribution FACTS devices. The objective of this design is to enhance the microgrid operation performance such as stabilize the buses voltage, reduce the feeder losses, improve the power factor and mitigate the harmonic distortion using the distribution FACTS devices. In the proposed design of microgrid which includes PV, fuel cell, battery, micro gas turbine, wind turbine, and diesel engine within microgrid, Switched Filter Compensator (SFC) is presented as AC-side D-FACTS device and Green Plug Filter Compensator (GPFC) is introduced as DC-side D-FACTS devices. Optimization search method based particle swarm optimization (PSO) is developed to optimize the control gains of D-FACTS controllers to achieve an effective integration of distributed energy resources technologies within MGs and to achieve higher microgrid performance. The results shown are generated from MATLAB simulation of the presented microgrid, where comparisons of with and without FACTS modules are presented in grid connected and islanding operation modes. Results show superiority and improved performance using the presented distribution FACTS.

Tidal Current Turbine and Related Development Problems for Indonesia

Tidal Current Turbine will be an important device of renewable energy in the upcoming decades. Hydro energy, wind energy and solar energy will mostly utilized in Indonesia in order to fulfill the energy of remote villages and remote islands and optimizing the domestic energy availability. Regarding the small scale field research conducted in the year 2012 and 2013 in Indonesia, transmission system and low rpm alternator are encountered as a significant problems in order to develop the hydro and wind turbine engine. Both parts are not yet produced locally in Indonesia. Domestic production in step-up gear transmission system of ratio 1 : 30 to 1 : 60 and low rpm alternator will fundamentally required to gain creativity in the country.

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.

Simulation of a new grid-connected hybrid generation system with Stirling engine and wind turbine

A detail model including all mechanical and electrical aspects is necessary to fully study hybrid grid operation. In this paper, a new grid-connected hybrid generation system with a Stirling engine and a wind turbine, which are connected to a grid through a common dc bus, is presented. The Stirling is more efficient than photo voltaic array and its combination with the wind turbine can create an efficient hybrid system. Fatigue, Aerodynamics, Structures, and Turbulence and Simulink/MATLAB are used to model the mechanical parts of the wind turbine, Stirling engine, and electrical parts. Field oriented control method is developed on voltage source converter. Power signal feedback method is implemented to determine generators reference shaft speed in hybrid system. Permanent magnet synchronous generator is used in the wind turbine and Stirling engine. Simulation results show that a new hybrid generation system with Stirling and wind turbine can work like other hybrid system and has suitable performance.

HEAT BALANCE RESEARCH OF 3MW MARINE WIND TURBINE'S ENGINE ROOM

The increment of heat load in Wind Turbine's engine room could cause the system shut down. In this paper the heat balance of Wind Turbine's engine room is investigated according to the structure of the engine room, environmental operating requirements as well as heat-dissipating of different components of a 3MW Wind Turbine System. The heat load of the engine room is analyzed and the physical model is developed based on finite volume method. After that, the temperature distribution of the cabin is obtained under different conditions with/without the jet unit. The temperature distribution in engine compartments with different number and the arrangement of jet units is compared in this paper. Based on the analysis, the cabin cooling design is carried out to achieve the purpose of stopping heat gathering and keeping heat balance of the cabin. This work puts forward the heat balance maintenance and completes the corresponding research, which could be helpful for the optimization of large-capacity Wind Turbine cooling system.

A fuzzy environmental-technical-economic model for distributed generation planning

To determine the optimal size, location and also the proper technology of distributed generation (DG) units in distribution systems, a static fuzzy multiobjective model is proposed in this paper. The proposed model can concurrently optimize a number of conflicting and competing objective functions including economic, technical and environmental attributes. The economic function is the profit of a distribution company (DisCo) from selling the DG output power to its customers. The contribution of this model is the consideration of some DG marginal revenues in the economic function. Inclusion of marginal revenues would not only reduce the investment risks of DG technologies, but also would enable the optimal penetration of DG units. The proposed DG planning framework considers various DG technologies such as photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), gas turbine (GT) and diesel engine (DE). The system uncertainties (including those for the energy demand, energy price and DG technologies operating and investment costs) are modeled using fuzzy numbers. The numerical case studies have been carried out using the IEEE 37-node distribution test system to demonstrate the performance of the proposed DG planning model.

Fuzzy multiobjective model for distributed generation expansion planning in uncertain environment

Uncertainty is one of the important factors that increase risk of exact decision makings. Although power systems behave more probabilistic today and system risk cannot be avoided completely, it can be evaluated and managed to an acceptable extent in planning, design, and operation activities. This paper presents a fuzzy multiobjective model for distributed generation planning so that uncertainties are modeled using fuzzy numbers (trapezoidal form). The proposed fuzzy model is based on the risk of economic, technical, and environmental objectives as well as fuzzy values of investment and operation cost of DG units due to technology's progress and fuel price fluctuations in the future. This model determines the optimal time, location, size, and type of DG units in distribution networks, using a multiobjective genetic algorithm (NSGA-II). The technologies which are considered in this study are photovoltaic (PV), wind turbine (WT), fuel cell (FC), microturbine (MT), gas turbine (GT), and diesel engine (DE). The proposed model is applied on a typical distribution system (IEEE 37 node test feeder) to assess the efficiency of the approach. Copyright © 2010 John Wiley & Sons, Ltd.

Promotion strategy of clean technologies in distributed generation expansion planning

Distributed generation expansion planning (DGEP) has been frequently reported in the literature around the world. In this scope, renewable technologies which are considered as a kind of distributed generations are developing due to their environmental benefits. However, only a few renewable energies have proven to be competitive so far, while their economic viability is also limited to certain regions of the world. In this paper, an encouraging mechanism is proposed in favor of clean technologies in the planning process. This mechanism is defined based on a grant function of emission not polluted which is paid to DG owners to promote renewable and clean technologies. In the planning process, a multi-objective optimization algorithm is applied to produce a Pareto set of optimal planning schemes by taking into account different objective functions (cost and grant functions). The best planning scheme among the Pareto set is chosen based on a composite utility which are obtained through a Monte Carlo simulation of uncertain situations. Distributed generation technologies which are considered in this paper are conventional and renewable technologies, namely photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), gas turbine (GT), and reciprocal engine (RE). To assess the ability of the proposed method, a typical distribution system is used for expansion planning under two environmental scenarios.