Control Studies of DFIG based Wind Power Systems

Wind energy as an outstanding and competitive form of renewable energy, has been growing fast worldwide in recent years because of its importance to reduce the pollutant emission generated by conventional thermal power plants and the rising prices and the unstable supplies of fossil-fuel. However, in the development of wind energy, there are still many ongoing challenges. An important challenge is the need of voltage control to maintain the terminal voltage of a wind plant to make it a PV bus like conventional generators with excitation control. In the literature with PI controllers used, the parameters of PI controllers need to be tuned as a tradeoff or compromise among various operating conditions. In this work, a new voltage control approach is presented. In the proposed approach, the PI control gains are dynamically adjusted based on the dynamic, continuous sensitivity which essentially indicates the dynamic relationship between the change of control gains and the desired output voltage. Hence, this control approach does not require any good estimation or tuning of fixed control gains because it has the self-learning mechanism via the dynamic sensitivity. This also gives the plug-and-play feature of DFIG controllers to make it promising in utility practices. Another key challenge in power regulation of wind energy is the control design in wind energy conversion system (WECS) to realize the tradeoff between the energy cost and control performance subject to stochastic wind speeds. In this work, the chance constraints are considered to address the control inputs and system outputs, as opposed to deterministic constraints in the literature, where the chance constraints include the stochastic behavior of the wind speed fluctuation. Two different control problems are considered here: The first one assumes the wind speed disturbance’s distribution is Gaussian; the second one assumes the disturbance is norm bounded, and the problem is formulated as a min-max optimization problem which has not been considered in the literature. Both problems are formulated as semi-definite program (SDP) optimization problems that can be solved efficiently with existing software tools. And simulation results are provided to demonstrate the validity of the proposed method

State-space self-tuner for on-line adaptive control

Dynamic systems, such as flight vehicles, satellites and space stations, operating in real environments, constantly face parameter and/or structural variations owing to nonlinear behavior of actuators, failure of sensors, changes in operating conditions, disturbances acting on the system, etc. In the past three decades, adaptive control has been shown to be effective in dealing with dynamic systems in the presence of parameter uncertainties, structural perturbations, random disturbances and environmental variations. Among the existing adaptive control methodologies, the state-space self-tuning control methods, initially proposed by us, are shown to be effective in designing advanced adaptive controllers for multivariable systems. In our approaches, we have embedded the standard Kalman state-estimation algorithm into an online parameter estimation algorithm. Thus, the advanced state-feedback controllers can be easily established for digital adaptive control of continuous-time stochastic multivariable systems. A state-space self-tuner for a general multivariable stochastic system has been developed and successfully applied to the space station for on-line adaptive control. Also, a technique for multistage design of an optimal momentum management controller for the space station has been developed and reported in. Moreover, we have successfully developed various digital redesign techniques which can convert a continuous-time controller to an equivalent digital controller. As a result, the expensive and unreliable continuous-time controller can be implemented using low-cost and high performance microprocessors. Recently, we have developed a new hybrid state-space self tuner using a new dual-rate sampling scheme for on-line adaptive control of continuous-time uncertain systems

Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison

Modern automation systems rely on closed loop control, wherein a controller interacts with a controlled process, based on observations. These systems are increasingly complex, yet most controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning offers a way to extend PID controllers beyond their linear capabilities by using neural networks. However, such an extension comes at the cost of losing stability guarantees and controller interpretability. In this paper, we examine the utility of extending PID controllers with recurrent neural networks-namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of control systems and highlight how they can be a scalable and interpretable option for control systems. To do so, we provide an extensive study using four benchmark systems that represent the most common control engineering benchmarks. All control benchmarks are evaluated with and without noise as well as with and without disturbances. The neural PID controller performs better than standard PID control in 15 of 16 tasks and better than model-based control in 13 of 16 tasks. As a second contribution, we address the lack of interpretability that prevents neural networks from being used in real-world control processes. We use bounded-input bounded-output stability analysis to evaluate the parameters suggested by the neural network, thus making them understandable. This combination of rigorous evaluation paired with better interpretability is an important step towards the acceptance of neural-network-based control approaches. It is furthermore an important step towards interpretable and safely applied artificial intelligence

The strengthening of Islamic values on students through the metaphor of accepting death: an Indonesian perception

Death is a sure entity for every human that cannot be avoided in human life. The purpose of this research was to reveal that the usage of metaphor technique called, “The Acceptance of Death” in group counselling can improve Islamic values on Muslim students. This study employed an action research using The Kemmis Model with the stages of planning, action, observation, and reflection. This research implemented group counselling with metaphor technique of accepting death by students. The research subjects were 20 female students of State Islamic University of Sultan Syarif Kasim Riau who lived in the campus dormitory. The selection of the research subjects was done randomly by choosing the female students who were willing to join the group counselling activity. The research results showed that the practice of metaphor technique of “The Acceptance of Death” in the group counselling can strengthen the Islamic values and their characteristics as Muslims. They understand their previous mistakes and are willing to be better for the sake of their life. They have the commitment to become the best students and the best Muslims

Adaptive Control for Power System Voltage and Frequency Regulation

Variable and uncertain wind power output introduces new challenges to power system voltage and frequency stability. To guarantee the safe and stable operation of power systems, the control for voltage and frequency regulation is studied in this work. Static Synchronous Compensator (STATCOM) can provide fast and efficient reactive power support to regulate system voltage. In the literature, various STATCOM control methods have been discussed, including many applications of proportional–integral (PI) controllers. However, these previous works obtain the PI gains via a trial and error approach or extensive studies with a tradeoff of performance and applicability. Hence, control parameters for the optimal performance at a given operating point may not be effective at a different operating point. To improve the controller’s performance, this work proposes a new control model based on adaptive PI control, which can self-adjust the control gains during disturbance, such that the performance always matches a desired response in relation to operating condition changes. Further, a new method called the flatness-based adaptive control (FBAC), for STATCOM is also proposed. By this method, the nonlinear STATCOM variables can easily and exactly be controlled by controlling the flat output without solving differential equations. Further, the control gains can be dynamically tuned to satisfy the time-varying operation condition requirement. In addition to the voltage control, frequency control is also investigated in this work. Automatic generation control (AGC) is used to regulate the system frequency in power systems. Various control methods have been discussed in order to design control gains and obtain good frequency response performances. However, the control gains obtained by existing control methods are usually fixed and designed for specific scenarios in the studied power system. The desired response may not be obtained when variable wind power is integrated into power systems. To address these challenges, an adaptive gain-tuning control (AGTC) for AGC with effects of wind resources is presented in this dissertation. By AGTC, the PI control parameters can be automatically and dynamically calculated during the disturbance to make AGC consistently provide excellent performance under variable wind power. Simulation result verifies the advantages of the proposed control strategy