Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Efficient Learning Machines

Computer scienc

Software Fault Prediction and Test Data Generation Using Articial Intelligent Techniques

The complexity in requirements of the present-day software, which are often very large in nature has lead to increase in more number of lines of code, resulting in more number of modules. There is every possibility that some of the modules may give rise to varieties of defects, if testing is not done meticulously. In practice, it is not possible to carry out white box testing of every module of any software. Thus, software testing needs to be done selectively for the modules, which are prone to faults. Identifying the probable fault-prone modules is a critical task, carried out for any software. This dissertation, emphasizes on design of prediction and classication models to detect fault prone classes for object-oriented programs. Then, test data are generated for a particular task to check the functionality of the software product. In the eld of object-oriented software engineering, it is observed that Chidamber and Kemerer (CK) software metrics suite is more frequently used for fault prediction analysis, as it covers the unique aspects of object - oriented programming such as the complexity, data abstraction, and inheritance. It is observed that one of the most important goals of fault prediction is to detect fault prone modules as early as possible in the software development life cycle (SDLC). Numerous authors have used design and code metrics for predicting fault-prone modules. In this work, design metrics are used for fault prediction. In order to carry out fault prediction analysis, prediction models are designed using machine learning methods. Machine learning methods such as Statistical methods, Articial neural network, Radial basis function network, Functional link articial neural network, and Probabilistic neural network are deployed for fault prediction analysis. In the rst phase, fault prediction is performed using the CK metrics suite. In the next phase, the reduced feature sets of CK metrics suite obtained by applying principal component analysis and rough set theory are used to perform fault prediction. A comparative approach is drawn to nd a suitable prediction model among the set of designed models for fault prediction. Prediction models designed for fault proneness, need to be validated for their eciency. To achieve this, a cost-based evaluation framework is designed to evaluate the eectiveness of the designed fault prediction models. This framework, is based on the classication of classes as faulty or not-faulty. In this cost-based analysis, it is observed that fault prediction is found to be suitable where normalized estimated fault removal cost (NEcost) is less than certain threshold value. Also this indicated that any prediction model having NEcost greater than the threshold value are not suitable for fault prediction, and then further these classes are unit tested. All the prediction and classier models used in the fault prediction analysis are applied on a case study viz., Apache Integration Framework (AIF). The metric data values are obtained from PROMISE repository and are mined using Chidamber and Kemerer Java Metrics (CKJM) tool. Test data are generated for object-oriented program for withdrawal task in Bank ATM using three meta-heuristic search algorithms such as Clonal selection algorithm, Binary particle swarm optimization, and Articial bee colony algorithm. It is observed that Articial bee colony algorithm is able to obtain near optimal test data when compared to the other two algorithms. The test data are generated for withdrawal task based on the tness function derived by using the branch distance proposed by Bogdan Korel. The generated test data ensure the proper functionality or the correctness of the programmed module in a software

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

A Tent L\'evy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study

The "Curse of Dimensionality" induced by the rapid development of information science, might have a negative impact when dealing with big datasets. In this paper, we propose a variant of the sparrow search algorithm (SSA), called Tent L\'evy flying sparrow search algorithm (TFSSA), and use it to select the best subset of features in the packing pattern for classification purposes. SSA is a recently proposed algorithm that has not been systematically applied to feature selection problems. After verification by the CEC2020 benchmark function, TFSSA is used to select the best feature combination to maximize classification accuracy and minimize the number of selected features. The proposed TFSSA is compared with nine algorithms in the literature. Nine evaluation metrics are used to properly evaluate and compare the performance of these algorithms on twenty-one datasets from the UCI repository. Furthermore, the approach is applied to the coronavirus disease (COVID-19) dataset, yielding the best average classification accuracy and the average number of feature selections, respectively, of 93.47% and 2.1. Experimental results confirm the advantages of the proposed algorithm in improving classification accuracy and reducing the number of selected features compared to other wrapper-based algorithms

Procjena duljine traga pukotine razlomljene stijenske mase pomoću algoritma SVM (algoritma stroja potpornih vektora)

Jointed rock masses modeling needs the geometrical parameters of joints such as orientation, spacing, trace length, shape, and location. The rock joint trace length is one of the most critical design parameters in rock engineering and geotechnics. It controls the stability of the rock slope and tunnels in jointed rock masses by affecting rock mass strength. This parameter is usually determined through a joint survey in the field. Among the parameters, trace length is challenging because a complete joint plane within rock mass cannot be observed directly. The development of predictive models to determine rock joint length seems to be essential in rock engineering. This research made an effort to introduce a support vector machine (SVM) model to estimate rock joint trace length. The SVM is an advanced intelligence method used to solve the problem characterized by a small sample, non-linearity, and high dimension with a good generalization performance. In this study, three data sets from the sedimentary, igneous, and metamorphic rocks were organized, which location of joints on the scanline, aperture, spacing, orientation (D/DD), roughness, Schmidt rebound of the joint’s wall, type of termination, trace lengths in both sides of the scanline and joint sets were measured. The results of SVM prediction demonstrate that predicted and measured results are in good agreement. The SVM model-based results were compared with those obtained from field surveys. The proposed SVM model-based model was very efficient in predicting rock joint trace length values. The actual trace length could be estimated; thus, the expensive, difficult, time-consuming, and destructive joint surveys related to obscured joints could be avoided.Za modeliranje razlomljenih stijenskih masa potrebni su geometrijski parametri pukotina kao što su orijentacija, razmak, duljina pukotine, oblik i lokacija. Duljina pukotine stijene jedan je od najkritičnijih projektnih parametara u inženjerstvu stijena i geotehnici. Ona kontrolira stabilnost nagiba stijene i tunela u razlomljenim stijenskim masama te utjecaj na čvrstoću stijenske mase. Obično se utvrđuje terenskim istraživanjima. Istraživanje duljine pukotine zahtjevno je, jer se cjelovita duljina unutar stijenske mase ne može promatrati izravno. Razvoj modela za predviđanje duljine traga pukotine iznimno je važan u inženjerstvu stijena. U okviru ovoga istraživanja predstavljen je algoritam SVM (engl. support vector machine, hrv. stroj potpornih vektora) za procjenu duljine pukotine. Riječ je o naprednoj metodi koja se koristi za rješavanje problema maloga uzorka, nelinearnosti i višestrukih dimenzija, s dobrim svojstvima generalizacije problema. Priređena su tri skupa podataka iz taložnih, magmatskih i metamorfnih stijena, koji uključuju položaj pukotina na liniji uzorkovanja, te njihov otvor, razmak, orijentaciju, hrapavost, Schmidtov odbojni test na stijenke pukotine, tip završetka, duljine traga na obje strane linije uzorkovanja i skupove pukotina. Rezultati predloženoga algoritma pokazuju podudarnost predviđenih i izmjerenih rezultata dobivenih terenskim istraživanjima. Rezultati pokazuju da je predloženi algoritam vrlo učinkovit u predviđanju vrijednosti duljine traga pukotine stijenske mase. Njegovim korištenjem može se dobro procijeniti stvarna duljina traga pukotine te izbjeći skupa, teška, dugotrajna i agresivna istraživanja razlomljenih stijenskih masa

Study of Voltage Stability Using Intelligent Techniques

The continuous increase in the demand of active and reactive power in the power system network has limits as scope for network expansion many a times poses serious problems. The power system must be able to maintain acceptable voltage at all nodes in the system at a normal operating condition as well as post disturbance periods. Voltage instability is a serious issue in the system due to progressive and uncontrollable fall in voltage level. The research presented in this thesis is concerned with several facets of the voltage stability problem. The focus of this thesis is to improve the voltage stability of the system. The sensitivity analysis plays an important role as it monitors the nearness of the system towards the voltage collapse situation. The conventional offline data as well as the online data are processed to determine the weak areas are determined. As the system is having nonlinearities it is governed by differential and algebraic equations which are in turn solved by nonlinear techniques. In this work the system is analysed with steady state model. Once the system is represented in the form of differential equations and standard form is achieved advanced control techniques can be easily applied for its solution. The main focus of this thesis is aimed at placing FACTS device known as the Static compensator (STATCOM) at weak location of the system network to address the problem of voltage instability. With its unique capability to control reactive power flow in a transmission line as well as voltage at the bus where it is connected, this device significantly contribute to improve the power system. These features turn out to be even more prominent because STATCOM can allow loading of the transmission lines close to their thermal limits, forcing the power to flow through the desired paths. This opens up new avenues for the much needed flexibility in order to satisfy the demands. The voltage instability is improved with reactive power supports of optimal values at optimal locations. Also renewable energy sources offer better option than the conventional types and hence attempt has been made to include the wind energy for this study the wind generator is considered delivering constant output and is assumed as a substitute to the conventional power generators. Finally the system voltage stability is studied with design of a controller based on probabilistic neural network. The developed controller has provided much better performance under wide variations in the system loads and contingencies and shown a significant improvement in the static performance of the system. The proposed controller is tested under different scenarios of line outages and the load increase and found to be more effective than the existing ones. The research has revealed a veritable cornucopia of research opportunities, some of which are discussed in the thesis