Copyright (c) 2008 All rights reserved. http://works.bepress.com/muhando Recent documents in Endusa Billy Muhando en-us Thu, 03 Jan 2008 11:28:39 PST 3600 http://works.bepress.com/muhando/9 http://works.bepress.com/muhando/9 Tue, 29 May 2007 17:09:33 PDT Although variable-speed operation can reduce the impact of transient wind gusts and subsequent component fatigue, this is still an unknown factor that must now be quantified. Reduction in drive-train stresses caused by fatigue loads in high wind turbulence is fundamental to realizing both output power levelling and long service life for a wind turbine generator (WTG). This paper presents an evolutionary controller comprising a linear quadratic Gaussian (LQG) and neurocontroller (NC) acting in tandem to effect optimal performance under high turbulence intensities. The control objectives are maximum energy conversion and reduction in mechanical stresses on the system components. The proposed paradigm utilizes generator torque in controlling the rotor speed in relation to the highly turbulent wind speed, thereby ensuring the extracted aerodynamic power is maintained at a constant value, while shaft moments are regulated. The performance of the proposed controller is compared with that of the LQG and it is found that the former is more efficient in maintaining rated power, minimizing shaft torque variations, and shows robustness to parameter variations. Endusa Billy Muhando Electric Power Generation Control (Wind energy) http://works.bepress.com/muhando/8 http://works.bepress.com/muhando/8 Tue, 29 May 2007 17:04:49 PDT Operation of wind turbine generator (WTG) systems in the above-rated region characterized by high wind turbulence intensities invariably induces fatigue stresses on the drive train components. This demands a trade-off between two performance metrics: maximization of energy harvested from the wind and minimization of the damage caused by mechanical fatigue. This paper presents a learning adaptive controller in the form of a self-tuning regulator (STR) for output power leveling and decrementing fatigue loads. The STR incorporates a hybrid controller of a linear quadratic Gaussian (LQG) and a neurocontroller (NC), and a linear parameter estimator (LPE). The main control objective is to regulate the relationship between rotational speed and wind speed by controlling the generator torque and further, the rotational speed. A pitch actuator ensures system operation geared toward maintaining output at rated power. A second-order model and a stochastic wind field model are used to systematically analyze the dynamical relationship between the WTG subsystems. The LQG is used as a basis upon which the performance of the proposed method in the trade-off studies is assessed. Simulation results indicate the proposed control scheme captures the performance and critical reliability loci thereby ensuring the wind turbine operates optimally in mechanically harmless conditions. Endusa Billy Muhando Electric Power Generation Control (Wind energy) http://works.bepress.com/muhando/7 http://works.bepress.com/muhando/7 Tue, 29 May 2007 16:51:25 PDT Probabilistic paradigms for wind turbine controller design have been gaining attention. Motivation derives from the need to replace outdated empirical-based designs with more physically relevant models. This paper proposes an adaptive controller in the form of a linear quadratic Gaussian (LQG) for control of a stall-regulated, variable speed wind turbine generator (WTG). In the control scheme, the strategy is twofold: maximization of energy captured from the wind and minimization of the damage caused by mechanical fatigue due to variation of torque peaks generated by wind gusts. Estimated aerodynamic torque and rotational speed are used to determine the most favorable control strategy to stabilize the plant at all operating points. The performance of the proposed controller is compared with the classical proportional-integral-derivative (PID) controller. The LQG is seen to be significantly more efficient especially in the alleviation of high aerodynamic torque variations and hence mechanical stresses on the plant drive train. Simulation results validate the effectiveness of the proposed method. Endusa Billy Muhando Electric Power Generation Control (Wind energy) http://works.bepress.com/muhando/6 http://works.bepress.com/muhando/6 Tue, 29 May 2007 16:46:05 PDT In order to perform maximum power point tracking control of wind generation system, it is necessary to drive windmill at an optimal rotor speed. For that purpose, a rotor position and a wind velocity sensors become indispensable. However, from the aspect of reliability and increase in cost, rotor position sensor and wind velocity sensor are not usually preferred. Hence, wind velocity and position sensorless operating method for wind generation system using observer is proposed in this paper. Moreover, improving the efficiency of the permanent magnet synchronous generator is also performed by optimizing d-axis current using the Powell method. Endusa Billy Muhando Electric Power Generation Control (Wind energy) http://works.bepress.com/muhando/5 http://works.bepress.com/muhando/5 Tue, 29 May 2007 16:37:58 PDT In this paper we present real-time neurocontrol system for a nonlinear dynamic plant. In order to improve the training and control performance we present a combined system with a neurocontroller (NC) and a linear quadratic regulator (LQR). We apply the control scheme to the backward control of a multi-trailer system. The function of the LQR is to cater for the linear part of the system thereby alleviating the load on the NC. An emulator of the plant is used to design the desired trajectory. The actual plant is subsequently run on this path. In the event that the plant fails to trace the desired trajectory, the control system is re-designed from this point and a new trajectory formulated. We utilize the GA to update the NC weights, while the evaluation function of the NC incorporates both the squared errors and the running steps errors; the latter having the function of realizing faster training of the NC. We have significantly reduced the computation time by utilizing one pattern training for the NCs in real time. Simulations show that the proposed online method has good control performance for the trailer truck system. Endusa Billy Muhando Intelligent Control of Nonlinear Dynamical Systems http://works.bepress.com/muhando/4 http://works.bepress.com/muhando/4 Tue, 29 May 2007 16:27:36 PDT In this article we introduce the recessive gene model (RGM), as a tool in numerical function optimization with binary coded genetic algorithms (GAs). GAs are widely applied in many optimization problems, and usually their main setback is loss of diversity, leading to either evolutionary stagnation or premature convergence. The dual-gene system exploits local continuities in multivariable, multimodal functions, thereby ensuring optimal propagation and avoiding premature convergence. Our simulations show that the efficiency of RGM is superior to the usual analysis employing only dominant genes, that RGM performs better on small populations than the single dominant gene at the same computational cost, and that RGM occasionally performs the function of mutation. Endusa Billy Muhando Intelligent Technique for Optimization http://works.bepress.com/muhando/3 http://works.bepress.com/muhando/3 Mon, 05 Feb 2007 19:21:04 PST Endusa Billy Muhando Intelligent Control of Nonlinear Dynamical Systems http://works.bepress.com/muhando/2 http://works.bepress.com/muhando/2 Mon, 05 Feb 2007 19:01:33 PST In this article we introduce the recessive gene model (RGM), as a tool in numerical function optimization with binary coded genetic algorithms (GAs). GAs are widely applied in many optimization problems, and usually their main setback is loss of diversity, leading to either evolutionary stagnation or premature convergence. The dual-gene system exploits local continuities in multivariable, multimodal functions, thereby ensuring optimal propagation and avoiding premature convergence. Our simulations show that the efficiency of RGM is superior to the usual analysis employing only dominant genes, that RGM performs better on small populations than the single dominant gene at the same computational cost, and that RGM occasionally performs the function of mutation. Endusa Billy Muhando Intelligent Technique for Optimization http://works.bepress.com/muhando/1 http://works.bepress.com/muhando/1 Mon, 05 Feb 2007 17:15:35 PST In recent developments wind power has been gaining rapid usage as an alternative source of electrical power and there is need to formulate optimized control schemes for power generation. This paper presents a sensorless maximum power point tracking control methodology for a wind power generation system. For the sensorless vector control a sliding mode observer is utilized in the estimation of the rotor speed while the rotor position is estimated based on the flux linkage. The Powell method is introduced to improve the efficiency of the permanent magnet synchronous generator (PMSG) d-axis current optimization. To ensure robustness of the proposed paradigm to parameter variations, the windmill loss coefficients determining the optimal rotor speed are identified online. Results of simulations confirm the effectiveness of the proposed method. Tomonobu Senjyu Electric Power Generation Control (Wind energy)