Dynamic Neural Network-based Adaptive Inverse Optimal Control Design

This dissertation introduces a Dynamical Neural Network (DNN) model based adaptive inverse optimal control design for a class of nonlinear systems. A DNN structure is developed and stabilized based on a control Lyapunov function (CLF). The CLF must satisfy the partial Hamilton Jacobi-Bellman (HJB) equation to solve the cost function in order to prove the optimality. In other words, the control design is derived from the CLF and inversely achieves optimality when the given cost function variables are determined posterior. All the stability of the closed loop system is ensured using the Lyapunov-based analysis. In addition to structure stability, uncertainty/ disturbance presents a problem to a DNN in that it could degrade the system performance. Therefore, the DNN needs a robust control against uncertainty. Sliding mode control (SMC) is added to nominal control design based CLF in order to stabilize and counteract the effects of disturbance from uncertain DNN, also to achieve global asymptotic stability. In the next section, a DNN observer is considered for estimating states of a class of controllable and observable nonlinear systems. A DNN observer-based adaptive inverse optimal control (AIOC) is needed. With weight adaptations, an adaptive technique is introduced in the observer design and its stabilizing control. The AIOC is designed to control a DNN observer and nonlinear system simultaneously while the weight parameters are updated online. This control scheme guarantees the quality of a DNN\u27s state and minimizes the cost function. In addition, a tracking problem is investigated. An inverse optimal adaptive tracking control based on a DNN observer for unknown nonlinear systems is proposed. Within this framework, a time-varying desired trajectory is investigated, which generates a desired trajectory based on the external inputs. The tracking control design forces system states to follow the desired trajectory, while the DNN observer estimates the states and identifies unknown system dynamics. The stability method based on Lyapunov-based analysis is guaranteed a global asymptotic stability. Numerical examples and simulation studies are presented and shown for each section to validate the effectiveness of the proposed methods

Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems

The optimal reactive power dispatch (ORPD) problem is an important issue to assign the most efficient and secure operating point of the electrical system. The ORPD became a strenuous task, especially with the high penetration of renewable energy resources due to the intermittent and stochastic nature of wind speed and solar irradiance. In this paper, the ORPD is solved using a new natural inspired algorithm called the marine predators’ algorithm (MPA) considering the uncertainties of the load demand and the output powers of wind and solar generation systems. The scenario-based method is applied to handle the uncertainties of the system by generating deterministic scenarios from the probability density functions of the system parameters. The proposed algorithm is applied to solve the ORPD of the IEEE-30 bus system to minimize the power loss and the system voltage devotions. The result verifies that the proposed method is an efficient method for solving the ORPD compared with the state-of-the-art techniques

Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions

Large-scale wind turbines with a large blade radius rotates under fluctuating conditions depending on the blade position. The wind speed is maximum in the highest point when the blade in the upward position and minimum in the lowest point when the blade in the downward position. The spatial distribution of wind speed, which is known as the wind shear, leads to periodic fluctuations in the turbine rotor, which causes fluctuations in the generator output voltage and power. In addition, the turbine torque is affected by other factors such as tower shadow and turbine inertia. The space between the blade and tower, the tower diameter, and the blade diameter are very critical design factors that should be considered to reduce the output power fluctuations of a wind turbine generator. To model realistic characteristics while considering the critical factors of a wind turbine system, a wind turbine model is implemented using a squirrel-cage induction motor. Since the wind speed is the most important factor in modeling the aerodynamics of wind turbine, an accurate measurement or estimation is essential to have a valid model. This paper estimates the average wind speed, instead of measuring, from the generator power and rotating speed and models the turbine’s aerodynamics, including tower shadow and wind shear components, without having to measure the wind speed at any height. The proposed algorithm overcomes the errors of measuring wind speed in single or multiple locations by estimating the wind speed with estimation error less than 2%

Real-Time Reliability Monitoring of DC-Link Capacitors in Back-to-Back Converters

Electrolytic capacitors have large capacity, low price, and fast charge/discharge characteristics. Therefore, they are widely used in various power conversion devices. These electrolytic capacitors are mainly used for temporary storage and voltage stabilization of DC energy and have recently been used in the renewable energy field for linking AC/DC voltage and buffering charge/discharge energy. However, electrolytic capacitors continue to be disadvantageous in their reliability due to their structural weaknesses due to the use of electrolytes and very thin oxide and dielectric materials. Most capacitors are considered a failure when the capacitance has changed by 25% of its initial value. Accurate and fast monitoring or estimation techniques are essential to be used with low cost and no extra hardware. In order to achieve these objectives, an online, reliable, and high-quality technique that continuously monitors the DC-link capacitor condition in a three-phase back-to-back converter is proposed. In this paper, the particle swarm optimization (PSO)-based support vector regression (PSO-SVR) approach is employed for online capacitance estimation based on sensing or deriving the capacitor current. Because the SVR performance alone severely depends on the tuning of its parameters, the PSO algorithm is used, which enables a fast online-based approach with high-parameter estimation accuracy. Experimental results are provided to verify the validity of the method

Nonlinear Control Strategies for Enhancing the Performance of DFIG-Based WECS under a Real Wind Profile

Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10−3 (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10−4 (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters