A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

Observation of charge ordering signal in monovalent doped Nd0.75Na0.25-xKxMn1O3 (0 ≤ x ≤ 0.10) manganites

K doping in the compound of Nd0.75Na0.25-xKxMn1O3 (x = 0, 0.05 and 0.10) manganites have been investigated to study its effect on crystalline phase and surface morphology as well as electrical transport and magnetic properties. The structure properties of the Nd0.75Na0.25- xKxMnO3 manganite have been characterized using X-ray diffraction measurement and it proved that the crystalline phase of samples were essentially single phased and indexed as orthorhombic structure with space group of Pnma. The morphological study from scanning electron microscope showed there was an improvement on the grains boundaries and sizes as well as the compactness with K doping suggestively due to the difference of ionic radius. On the other hand, DC electrical resistivity measurement showed all samples exhibit insulating behavior. However, analysis of dlnρ/dT-1 vs. T revealed the clearly peaks could be observed at temperature 210K for x = 0 and the peaks were shifted to the lower temperature around 190 K and 165 K for x = 0.05 and x = 0.1 respectively, indicate the existence of charge ordering (CO) state in the compound. Meanwhile, the investigation on magnetic behavior showed all samples exhibit transition from paramagnetic phase to anti-ferromagnetic phase with decreasing temperature and the TN was observed to shift to lower temperature suggestively due to weakening of CO stat

Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge

This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is trained. In the latter, the trained DNN, which mimics the conventional controller, controls the system. Unlike the existing papers in the literature, the network is still being trained for different sets of trajectories which are not used in the training phase of DNN. Thanks to the rule-base, which contains the expert knowledge, the proposed framework learns the system dynamics and operational uncertainties in real-time. The experimental results show that the proposed online learning-based approach gives better trajectory tracking performance when compared to the only offline trained network.Comment: corrected version accepted for ICRA 201

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

Promjena indikatora kvalitete električne energije trošila predstavljanjem adaptivne metode za upravljanje DVR-om zasnovane na Hebbovom algoritmu učenja

Having electricity with high quality is one of the more important aims in electrical systems. Disturbances in distribution systems can change voltage waveform. There are some methods to prepare high power quality for sensitive loads. In this research we use “Dynamic Voltage Restorer” to compensate the harmful effects of disturbances on voltage. Since power systems fundamentally have complicated dynamic behavior, especially during faults, “Hebb” learning self-tuning controller, which is a powerful adaptive controller, has been used. In order to improve the performance of this controller from point of view of power quality’s indices, such as flash and sensitive load voltage THD, a new structure is proposed for this controller with fuzzification method. Simulation results indicate better operation of the system for the case of proposed controller. Voltage sag and harmonics in faulty conditions are both improved by the proposed controller. According to simulation results, it works better than both classical PI controller and conventional Hebb learning controller.Jedan od važnijih ciljeva elektroenergetskog sustava visoka je kvaliteta električne energije. Poremećaji u distribucijskom sustavu mogu neželjeno izmijeniti valni oblik napona. Postoji nekoliko metoda kako osigurati visoku kvalitetu energije za osjetljiva trošila. U istraživanju koristimo "dinamičku obnovu napona" za kompenziranje štetnih efekata poremećaja u naponu. Kako energetski sustavi u osnovi imaju složeno dinamičko ponašanje, posebno tijekom kvarova, korišten je vrlo moćan adaptivni regulator: "Hebbov" samopodešavajući regulator sa sposobnošću učenja. Da bi se unaprijedilo vladanje spomenutog regulatora s aspekta indikatora kvalitete energije kao što su parcijalna izbijanja i THD osjetljivog trošila, predložena je nova struktura regulatora s uključenim metodama neizrazite logike. Simulacijski rezultati pokazuju bolji rad sustava uz korištenje predloženog regulatora. Regulator smanjuje propade napona i poboljšava harmonični sastav sustava u kvarnim uvjetima. Rezultati simulacija također pokazuju bolje ponašanje u odnosu na uobičajeni PI regulator te konvencionalni Hebbov regulator s učenjem