Showing papers in "Journal of Control, Automation and Electrical Systems in 2018"

A Contribution to the Optimization of the Reconfiguration Problem in Radial Distribution Systems

Abstract: Optimal reconfiguration is one of the most important strategies used to improve the operation of radially operated distribution systems. A large amount of research related to the reconfiguration problem has been carried out. Nevertheless, some topics still need to be discussed in order to provide guidance for future research related to the reconfiguration problem, such as the corresponding search space of the problem, optimization models that can be solved using exact optimization techniques, and the efficient implementation of metaheuristics. This paper addresses the following topics related to the reconfiguration problem: a detailed analysis of the search space of the problem and its relevance for developing efficient metaheuristics, an analysis of exact optimization models and their importance in the current context of optimizing the reconfiguration problem, and a detailed analysis of the main operators for the use of metaheuristics applied to the reconfiguration problem. Finally, the optimal solutions of the most analyzed systems in the literature are presented. Also, it is concluded that there is a need to set up a new database of larger and more complex systems to be used as a benchmark for new optimization proposals toward the solution of the reconfiguration problem.

A Real Test System For Power System Planning, Operation, and Reliability

Abstract: Nowadays, several test systems available in the specialized literature are used to verify studies regarding power system planning or network reliability. However, there are no test systems currently available with enough information in order to endorse studies that simultaneously approach expansion planning, operation, and reliability issues. This paper introduces a real test system, including the load modeling, and generation and transmission systems. The main objective is to provide all the details and information required to evaluate methods and models developed for power system planning, operation, and reliability. The presented load modeling includes hourly, daily, weekly, monthly, and seasonal patterns. Furthermore, besides the substation data, reliability details, construction costs, and characteristics of right of ways (e.g., line length, impedance, and ratings) for the transmission system are exposed. The real transmission system presented contains 39 buses, 135 transformers, and 66 lines at two voltage levels: 230 and 400 kV. Finally, the generation system reliability data as well as operation and installation costs for each unit are also provided.

Design of Wide-Area Damping Controllers Incorporating Resiliency to Permanent Failure of Remote Communication Links

Abstract: The synchrophasor data provided by wide-area measurement systems have potential applications in electric power systems such as the use of these measurements as control inputs of wide-area damping controllers (WADCs) for small-signal stability enhancement. However, synchrophasor data are particularly vulnerable to cyber attacks (such as denial-of-service attacks) that can cause communication link failures in a smart grid communication network and, consequently, compromise the power system stability. In order to reduce the impact of communication failures on the performance of a WADC, this paper proposes a method to design a WADC considering robustness to multiple operating points, time delays in the communication channels and possible permanent loss of communication signals in the input and the output of the controller (which may be due, e.g., to denial-of-service cyber attacks). The performance of the designed controller is evaluated using modal analysis and nonlinear time-domain simulations in one of the IEEE benchmark systems to validate the results: the Simplified 14-Generator Model of the Southeastern Australian Power System.

Optimal PMU Placement Using Modified Greedy Algorithm

Abstract: Phasor measurement units (PMUs) provide synchronized measurements of real-time phasors of voltages and currents. It is considered as an important element of the smart wide area measurement system used in advanced power system monitoring, protection, and control applications. This paper proposes a new approach based on a greedy algorithm to solve the optimal phasor measurement unit placement (OPP) problem in the power network. The main purpose of proposed approach is to find out a high-quality solution in a reasonable time that ensures the practicability when applying for a real power network. The OPP problem is solved under both normal operating and contingency conditions. Moreover, some other realistic aspects that may affect the OPP problem, such as PMU channel limitation, zero injection bus, the presence of conventional measurements, are also considered to solve simultaneously. The simulations on IEEE 14-bus, 30-bus, 57-bus, 118-bus test systems, and especially on a large-scale network—the Polish 2383-bus system, are presented for evaluating the feasibility of proposed approach. The results of this study showed that the proposed method is effective and feasible to solve the OPP problem for a real power network.

A Simple Optimization Method for Tuning the Gains of PID Controllers for the Autopilot of Cessna 182 Aircraft Using Model-in-the-Loop Platform

Abstract: Currently, there is a growing demand for automatic control systems for unmanned aerial vehicles due to the numerous civil and military applications. An unmanned aerial vehicle has sophisticated and complex controllers that are used to stabilize it, which composes the autopilot. In autopilots, PID controllers are commonly used, and various techniques are applied to tune their gains. In this paper are proposed optimization procedures of gains for the designed PID controllers from the transfer functions simulated in the Matlab/Simulink software, establishing a model-in-the-loop system, for the autopilot of the Cessna 182 aircraft. In this context, results of simulations are obtained to prove the effectiveness of using these proposals for optimization. They offer a simple, effective, systematic and replicable way to obtain the gains and dispense the use of classical methods of determination of gains for control loops, as well as the trial and error method.

DSCC-MMC STATCOM Main Circuit Parameters Design Considering Positive and Negative Sequence Compensation

Abstract: The double-star chopper cell modular multilevel converter (DSCC-MMC) has been employed in several applications as HVDC, energy storage, renewable energy, electrical drives and STATCOMs. Generally, the DSCC-MMC main circuit parameter design presented in literature considers balanced currents flowing through the converter. Nevertheless, in STATCOM application, the converter can compensate negative sequence components and unbalanced currents flow through the DSCC-MMC, resulting in different stresses in the converter phases. Therefore, this work presents a detailed design methodology of the DSCC-MMC main circuit parameters, considering both positive and negative sequence current compensations. The dc-link voltage, number of submodules, power semiconductor thermal stresses, submodule capacitance and arm inductances are designed. Expressions for the energy storage requirements are derived when negative sequence is compensated. A case study considering a 15-MVA STATCOM is presented, and simulation results validate the proposed design methodology. Finally, the converter power losses and thermal stresses in the power semiconductors are evaluated.

Reduced-Order Model Approximation of Fractional-Order Systems Using Differential Evolution Algorithm

Abstract: In this paper, we authors propose to use an optimization technique known as Differential Evolution (DE) optimizer for the approximation of fractional-order systems with rational functions of low order. Usual integer-order models with eleven unknown parameters are optimized to represent non-integer-order systems using the DE algorithm. Four numerical examples have illustrated the efficiency of the proposed reduced-order approximation algorithm. The results obtained from the DE approach were compared with those of Oustaloup and Charef approximation techniques for fractional-order transfer functions. They showed clearly that the proposed approach provides a very competitive level of performance with a reduced model order and less parameters.

Optimal Distributed Generation Allocation Using a New Metaheuristic

Abstract: The optimal placement of distributed generation in power distribution systems problem consists of defining the most appropriate sites to install those generators and their optimal sizing, aiming to optimize the system performance. From the mathematical point of view, it is a complex nonlinear optimization problem, containing continuous and discrete variables. The present paper deals with the optimal placement and sizing of distributed generators for real power losses minimization in distribution systems. For this purpose, a new metaheuristic approach called War Optimization is proposed. The best sites to place these generators are defined by the War Optimization method, and the sizing is given by an optimal power flow tool. A set of simulations is run using radial distribution test systems containing 69 and 476 busbars. A detailed comparison between War Optimization and other metaheuristics (including Particle Swarm Optimization) shows that the proposed method is efficient, as it presents better solutions more often.

Design of Coordinated Decentralized Damping Controllers for Power Systems Considering Uncertainties

Abstract: This paper presents an analytical method based on the solution of the nonlinear Riccati equation for the design of coordinated, decentralized damping controllers for power systems. While other approaches use analytical methods that ensure robustness with respect to uncertainties in the system operating conditions by considering a controller of the same order as the plant, which results in high-order controller structures, the proposed method has the advantage of providing robust and decentralized low-order controllers. The method is applied to two IEEE benchmarks, and the designed controllers are assessed by modal analysis and nonlinear time-domain simulation.

A Neuro-Fuzzy Approach for Locating Broken Rotor Bars in Induction Motors at Very Low Slip

Abstract: Squirrel-cage induction motors are widely used in a number of applications throughout the world. This paper proposes a neuro-fuzzy approach to identify and to classify a typical fault related to the induction motor damage, such as broken rotor bars. Two fuzzy classifiers are obtained by an adaptive-network-based fuzzy inference system model whose parameters can be identified by using the hybrid learning algorithm. A Hall effect sensor was installed between two stator slots of the induction machine, and a magnetic flux density variation is measured according to the failure. The data from the Hall sensor were used to extract some harmonic components by applying fast Fourier transform. Thus, some frequencies and their amplitudes were considered as inputs for the proposed fuzzy model to detect not only adjacent broken bars, but also noncontiguous faulted scenarios. In the present work it is not necessary to estimate the rotor slip, as required by the traditional condition monitoring technique, known as motor current signature analysis. This method was able to detect broken bars for induction motor running at low-load or no-load condition. The intelligent approach was validated using some experimental data from a 7.5-kW squirrel-cage induction machine.

Optimal Power Flow For Multi-FACTS Power System Using Hybrid PSO-PS Algorithms

Abstract: This paper presents an efficient hybrid particle swarm optimization and pattern search algorithms for the solution of optimal power flow problem with inclusion of flexible AC transmission systems (FACTS) devices. The purpose of the proposed technique is to merge the advantages of both PSO in exploitation and PS in exploration to achieve the best solution. The FACTS devices considered in this study are thyristor-controlled series compensator and static var compensator. These devices aim to minimize the total generation cost, enhance voltage deviation, decrease real and reactive losses and improve the voltage stability index. The proposed method is carried out on the standard IEEE 30-bus system, and obtained results are compared to other methods reported in the literature. Simulation results demonstrate the effectiveness and potential of the proposed method.

New Relaxed Stability Conditions for Uncertain Two-Dimensional Discrete Systems

Abstract: This paper is concerned with the problem of robust stability of uncertain two-dimensional (2-D) discrete systems described by the Roesser model with polytopic uncertain parameters. Based on a newly developed parameter-dependent Lyapunov–Krasovski functional combined with Finsler’s lemma, new sufficient conditions for robust stability analysis are derived in terms of linear matrix inequalities (LMIs). Numerical examples are given to show the effectiveness and less conservatism of the proposed results.

Dynamic Inversion and Gain-Scheduling Control for an Autonomous Aerial Vehicle with Multiple Flight Stages

Abstract: Several configurations of unmanned aerial vehicles (UAVs) were proposed to support different applications. One of them is the tailsitter, a fixed-wing aircraft that takes off and lands on its own tail, with the advantage of high endurance from fixed-wing aircraft and not requiring a runway during takeoff and landing as helicopters. However, the flight envelope of these vehicles contains multiple flight stages, each one with its own particularities and requirements, which makes its control more complex and hampers its use as an autonomous vehicle. Therefore, this paper presents an autopilot structure for a new tailsitter UAV, called Autonomous VerticAL takeOff and laNding (AVALON), with focus in the use of dynamic inversion and gain-scheduling control. Moreover, the AVALON’s equations of motion are described and used to reproduce a simulation environment. The results show the feasibility of these control techniques, with a convergence of the aircraft attitude and velocity during all the different flight stages of AVALON’s operation. We also compared its behaviour with PI controllers that calculates the control surfaces deflections with the attitude, and the use of dynamic inversion with gain-scheduling shows smaller errors in most of the flight stages, with the exception of the horizontal and landing stages.

A NILM Dataset for Cognitive Meters Based on Conservative Power Theory and Pattern Recognition Techniques

Abstract: This paper presents a novel dataset capable of classifying and disaggregating residential appliances for the development of smart or cognitive power meters. This novel dataset uses power indicators (also denoted as conformity factors) from the conservative power theory (CPT), which are calculated from measured voltage and current waveforms during the operation of residential loads. The association of CPT power indicators with suitable pattern recognition algorithms (PRA) and a power signature state machine provides proper identification of each appliance. So, the paper also presents a detailed evaluation of possible PRA for this application, especially the SVM—support vector machine, OPF—optimum-path forest, MLP—multilayer perceptron, KNN—K-nearest neighbor and DT—decision tree. All these algorithms have been compared regarding accuracy and computational time. Validation results point out that KNN would be the best choice for dealing with the proposed dataset, leading to an accuracy higher than 98%.

ILP-Based Optimal PMU Placement with the Inclusion of the Effect of a Group of Zero-Injection Buses

Abstract: Zero-injection buses (ZIBs) can reduce the number of phasor measurement units (PMUs) required to be installed for complete observability. It has been demonstrated that a group of neighboring ZIBs can further reduce this required number of PMUs. In contrast to a single ZIB, the effect of a group of ZIBs has not been incorporated into the model of optimal PMU placement (OPP) using mathematical approaches, except for some heuristics methods. In this paper, a novel methodology to incorporate the effect of a group of ZIBs into the OPP model is proposed, using the integer linear programming approach. Two common contingencies—line outage and PMU loss—are also considered. Moreover, two visualization techniques are used for illustrating the locations of PMUs obtained from OPP: (1) using a graph-based force-directed method and (2) on a map using the geographic information system. The proposed method is verified using several small- and large-scale test systems and compared with other related studies.

Humanoid Robot Framework for Research on Cognitive Robotics

Abstract: This paper presents a humanoid robot framework, composed of a simulator and a telemetry interface. The framework is based on the Cross Architecture, and it is developed aiming for the RoboCup Soccer Humanoid League domain. A simulator is an important tool for testing cognitive algorithms without handling issues of real robots; furthermore, a simulator is extremely useful for allowing reproducibility of any developed algorithm, even if there is no robot available. The proposed simulator allows an easy transfer of the algorithms developed in the simulator to real robots, as long as it uses the Cross Architecture as its software architecture. Then, in order to evaluate the cognitive algorithms in real robots, a telemetry interface is proposed. From this interface, it is possible to monitor any variable in the robot’s shared memory. The framework is open source and has low computational cost. Experiments were conducted in order to analyze both, simulator and telemetry interface. Experiments performed with the simulator aim to validate the high-level strategy development and the portability to a real robot, while experiments with telemetry interface aim to evaluate the robot behavior using, as input, the information received from the shared memory passed by all processes. The results show that the simulator can be used to test and develop new algorithms, while the telemetry can be used to monitor the robot, thus validating the framework for this domain.

A Comparative Study Between Cascaded FOPI–FOPD and IOPI–IOPD Controllers Applied to a Level Control Problem in a Coupled Tank System

Abstract: This paper presents a novel liquid level control of a coupled two tank SISO system. Physical system is modeled from the open loop experimental response in two stages. A cascade control strategy is adopted. Fractional Order Proportional Integral (FOPI) and Fractional Order Proportional Derivative (FOPD) controllers are designed for the outer and inner loop, respectively, using frequency domain method. Similarly, conventional Integer Order Proportional Integral and Integer Order Proportional Derivative controllers are also designed to compare the performances. Reference signal tracking performance is tested with multiple step, ramp and sinusoidal reference signals. Ability of disturbance rejection is tested by adding more water into the tanks through a disturbance input channel. Experimental results reveal that the proposed FOPI–FOPD control scheme outperforms its integer order counterparts both in terms of reference signal tracking and disturbance rejection.

Short-Term Scheduling of Integrated Power and Spinning Reserve of a Wind-Hydrothermal Generation System with AC Network Security Constraints

Abstract: The world energy matrix has diversified and has become a mix of hydroelectric, thermoelectric and renewable sources, such as wind energy. However, wind power is uncertain and variable, and its random intermittence leads to great challenges in coordinating it with a large hydrothermal system, for example. These questions require increased availability of spinning reserve as a solution to reduce the risk of deficit in moments when there is no wind power generation. This spinning reserve must be appropriately allocated between the hydraulic and thermal generating units so that, when necessary, they will be available and operational. To carry out this adequate allocation, besides considering the conventional operational limits of a problem of generation dispatch, it is also necessary to take into consideration the limits of the interchange lines that connect the subsystems that compose the electric network. So, in cases of congestion of these transmission lines, the subsystem itself can supply its spinning reserve under different hydrological conditions. Thus, this work proposes a mathematical formulation to dispatch power generation and allocate spinning reserve simultaneously, considering different hydrological day-ahead conditions. To do this, a nonlinear and dynamic optimal power flow is modeled, which, in addition to performing the active and reactive power dispatch of a hydrothermal system (including electrical and energy restrictions) for a day-ahead horizon, is also capable of carrying out an optimal allocation of the spinning reserve (hydraulic and thermal). The model is tested using a system of 33 buses to represent the system of the southern region of Brazil.

A Response Surface Model Approach to Parameter Estimation of Reinforcement Learning for the Travelling Salesman Problem

Abstract: This paper reports the use of response surface model (RSM) and reinforcement learning (RL) to solve the travelling salesman problem (TSP). In contrast to heuristically approaches to estimate the parameters of RL, the method proposed here allows a systematic estimation of the learning rate and the discount factor parameters.The Q-learning and SARSA algorithms were applied to standard problems from the TSPLIB library. Computational results demonstrate that the use of RSM is capable of producing better solutions to both symmetric and asymmetric tests of TSP.

Extraction and Classification of Human Body Parameters for Gait Analysis

Abstract: Human gait analysis is considered a new biometric tool for the ability to obtain metrics from the body at a distance. Biometric identifiers have properties that can technologically measure and analyze the characteristics of the human body and can be used as a form of identification and access control for security applications. Recognition through proper interpretation of gait parameters has become a relevant pattern classification problem. This work aims to develop an image processing system, with the use of the Microsoft Kinect sensor, which is capable to extract movement patterns for gait analysis and to present a comparative study of different pattern recognition methods for human identification. The image processing system, developed in C#, allowed the acquisition of three-dimensional data from several volunteers and made it possible to identify the human skeleton and automatically extract the kinetic and kinematic parameters of the body. For data analysis, different classification methods were compared. Among them, the algorithms that presented better performance were probabilistic neural networks, deep neural networks and k-nearest neighbors, with nearly 99% correct recognition rate. The obtained results demonstrate the efficiency of gait analysis as a biometric method. They also show the viability of gait parameter extraction using the Kinect sensor and the good performance of pattern recognition methods applied to the acquired gait kinetic and kinematic parameters.

Analysis of Decision Stochastic Discrete-Event Systems Aggregating Max-Plus Algebra and Markov Chain

Abstract: Many optimization problems are complex enough that their solutions must be measured through simulation. It is also known that simulation requires a huge computational effort which impacts directly on the optimization solution. Accordingly, this paper presents a hybrid methodology faster than standard simulation tools to deal with stochastic systems subject to synchronization, delay, and decision phenomena. Such methodology aggregates Max-Plus Algebra with Markov Chain for modeling a load haulage cycle of an open-pit mine. The goal is computing the expected value for total iron production. To show that this new methodology can be applied to compute the mentioned measure, an experiment analysis was conducted to compare the results obtained. The test has shown evidence of equivalence between the results acquired by the hybrid methodology and by a standard simulation tool.

A Multi-objective Approach for Optimized Monitoring of Voltage Sags in Distribution Systems

Abstract: Voltage sags are among the most relevant power quality disturbances. Furthermore, they also have high occurrence rates. Their stochastic nature makes monitoring difficult and causes significant losses to power utilities and customers. This paper presents an approach to overcome the problem of allocating power quality monitors. To do so, our approach accounts for topological coverage, unmonitored voltage sags, and the total cost of required equipment. We used NSGA-II to build our approach due to its efficiency in dealing with combinatorial problems. We also used the Monte Carlo simulation method to model the time series in our approach due to the random nature of power quality disturbances. To evaluate our approach, we simulated the IEEE 13-, 34- and 37-bus distribution systems using the DigSILENT Power Factory 15.1 software. The evaluation results show that our approach supported cost reduction associated with the installation of power quality monitors, both in terms of identifying adequate number and position of the performance monitors.

Synchronization of the Chaotic Fractional-Order Genesio–Tesi Systems Using the Adaptive Sliding Mode Fractional-Order Controller

Abstract: This paper introduces an adaptive fractional-order sliding mode controller for synchronization of two chaotic Genesio–Tesi systems with fractional dynamics. For this purpose, first the error dynamics is defined; then the adaptive sliding mode synchronization controller is designed through defining suitable sliding surface and estimating uncertainty parameters and stability of the control system is verified using related theorems. Robustness against input uncertainty and disturbance is considered as a primary design objective. Simulation results using MATLAB show that the proposed adaptive fractional-order sliding mode controller is able to synchronize the mentioned chaotic system with acceptable performance.

Robust and Coordinated Tuning of PSS and FACTS-PODs of Interconnected Systems Considering Signal Transmission Delay Using Ant Lion Optimizer

Abstract: This paper presents an ant lion optimizer (ALO) that is used to solve the robust and coordinated tuning of power system stabilizers (PSS) and the power oscillation damping (POD) controller of flexible AC transmission system (FACTS) devices in the presence of remote signals in multimachine power systems. The remote signals are used for the damping of interarea oscillation modes and are modeled by Pade approximation. The static var compensator and thyristor-controlled series capacitor, two FACTS most deployed in practical applications, were considered in this study. The ALO algorithm mimics the hunting mechanism of ant lions in nature: where four steps of hunting prey such as entrapment of ants in traps, random walk of ants, elitism and catching preys/re-building traps are implemented. The two test systems which have been used for the application of the proposed methodology for tuning of PSS and FACTS-PODs are the New England–New York 16-generator 68-bus system, and the Brazilian equivalent system modeled with 24 synchronous machines and 107 buses. Results from these simulations demonstrate the applicability of the proposal in which the efficiency of ALO is highlighted as compared to other algorithms used for design of PSS and FACTS-PODs such as particle swarm optimization and sequential quadratic programming.

Exponential State Observers for Nonlinear Systems with Incremental Quadratic Constraints and Output Nonlinearities

Abstract: This paper considers the exponential observer design for a class of nonlinear systems with output nonlinearities. The nonlinear terms in the systems are assumed to satisfy incremental quadratic constraints which include many commonly encountered nonlinearities in existing literature as some special cases. We construct a circle-criterion-based observer by injecting both the linear and the nonlinear output error terms into the observer system. Sufficient conditions ensuring the exponential stability of the proposed observer are established and formulated in terms of linear matrix inequalities. Finally, the advantages and effectiveness of the proposed design approach are illustrated through two examples.

Distributed Fault Detection and Isolation of Actuator Faults in Multi-agent Systems with Complex-Weights Directed Communication Topology

Abstract: In this paper, the problem of distributed fault detection and isolation (FDI) for multi-agent systems (MAS) with complex-weights directed communication topology in the presence of actuator faults is studied. It is assumed that each agent only has access to local state information of neighboring agents. The communication network is also assumed to be a weights directed graph (digraph) whose edges are attributed with complex weights. First, a novel representation of the complex-weighted graph is proposed. More specifically, the difficulty with complex coefficient in the problem of FDI for MASs with complex-weights directed communication topology is indeed resolved. Then, using the novel representation, a bank of observers is constructed to compute residual vectors with an aim of allowing the FDI of actuator fault to occur on any agent of the MASs. It is proved that the proposed FDI scheme is capable of FDI in the MASs with complex-weights directed communication topology. Finally, some numerical example results are provided to demonstrate the effectiveness of the proposed scheme.

Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation

Abstract: This paper assesses different applied pattern recognition algorithms to decide the most appropriate power factor compensator for a particular point of common coupling. Power factor, current unbalance factor, total demand distortion, voltage harmonic distortion and reactive power daily variation, as well as human expertise, are the key parameters used to set each recognition algorithm. These algorithms are then trained with a series of both simulation and experimental data. Numerical results consistently indicate the decision-tree algorithm with depth 20 as the best classifier for power factor improvement in terms of all metrics considered in this work.

Bayesian Network and Compact Genetic Algorithm Approach for Classifying Partial Discharges in Power Transformers

Abstract: This paper presents a statistical learning method capable of classifying the incidence level of partial discharges in power transformers. By using the results from acoustic emission measurements, it is possible to detect the presence of partial discharges inside the equipment, allowing the qualitative health monitoring of the transformer’s insulation. Therefore, the use of a Bayesian Network is proposed, combined with a Compact Genetic Algorithm tailored for solving mixed integer programming problems, for discretization of the continuous metrics extracted from acoustic emission measurement. Comparing the results with Multilayer Perceptron Neural Network and Decision Tree and after a suitable amount of runs of the algorithm, it was verified that the Bayesian Networks presented superior results.

Power Quality Study and Analysis of Different Arc Welding Machines

Abstract: Electrical welding machines are among the most pollutant loads due to their strong nonlinear behavior. As a result, utilities have established new criteria for connecting welding machines in the grid. One approach that can provide good results in this context is based on modeling this special load and performing simulation studies. In this paper, two different computational models were developed in PSCAD/EMTDC to help the connection evaluation process and power quality studies. Furthermore, instead of a conventional power quality analysis, a power theory for non-sinusoidal voltages and currents is needed to analyze three-phase welding machines. The conservative power theory (CPT) allows an advanced analysis of the load’s characteristics as it decouples the power factor into different load conformity factors that are associated with specific characteristics in the load. The analysis and discussions are based on simulations and experimental measurements obtained at the terminals of two different welding machines. The results help to demonstrate the main advantage of the CPT compared to conventional approaches, which is related to its general application for single-phase and three-phase disturbing loads under non-ideal supply voltage conditions.

Trajectory Optimization Using Sequential Convex Programming with Collision Avoidance

Abstract: In multi-robot systems, it is commonly used collaborative approaches to solve complex tasks faster and efficiently. In most of those approaches, the decisions are made centralized or require global information about the objective or the robots, limiting many real implementations. The present work is based on a decentralized solution for the Rendezvous problem, by using only local information about the robots and asymmetrical information about the meeting point. As the primary contribution, we propose a sequential convex programming approach to overcome the non-convexities when physical spaces are taken into account in the optimization problem, which provides the robots with the collision avoidance capability during their movement to the target point. Experiments are also performed to show the effectiveness of the proposed approach.