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There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

One of the biggest problems with distribution systems correspond to the load unbalance created by power demand of customers. This becomes a difficult task to solve with conventional methods. Therefore, this paper uses integer linear programming and Branch and Bound algorithm to balance the loads in the three phases of the distribution system, employing stored data of power demand. Results show that the method helps to decrease the unbalance factor in more than 10%, by selecting the phase where a load should be connected. The solution may be used as a planning tool in distribution systems applied to installations with systems for measuring power consumption in different time intervals. Furthermore, in conjunction with communications and processing technologies, the solution could be useful to implement with a smart grid.

This paper presents the dynamic analysis of a permanent magnet DC motor using a buck converter controlled by zero average dynamics (ZADs) and fixed-point inducting control (FPIC). Initially, the steady-state behavior of the closed-loop system was observed and then transient behavior analyzed while maintaining a fixed ZAD control parameter and changing the FPIC parameter. Other behaviors were studied when the value of the ZAD control parameter changed and the FPIC parameter was maintained at the initial value. Besides, bifurcation diagrams were built with one and two delay periods by changing the control parameter of the FPIC and maintaining fixed ZAD parameters while some disturbances were carried out in the electric source. The results show that the ZAD-FPIC controller allowed good regulation of the speed for different reference values. The ZAD-FPIC control technique is effective for controlling the buck converter with the motor, even with two delay periods. The robustness of the system was checked by changing the voltage of the source. It was shown that the system used a fixed switching frequency because the duty cycle was not saturated for certain ranges of the control parameters shown in the research. This technique can be used for higher order systems with experimental phenomena such as quantization effects, time delays, and variations in the input signal.

This paper presents the experimental implementation of a buck converter with quasi-sliding mode control combined with a loss estimator function. An online loss estimator is developed to estimate, in real time, the parasitic resistances of the converter and variations of the resistance in the load. The estimated loss resistance and the resistance of the load are embedded, in real time, into the model equations of the controller using Zero Average Dynamics and Fixed Point Induction Control techniques (ZAD-FPIC) to improve the control robustness to resistive parameter variations. Details of the experimental setup are presented to show developed electrical and electronic circuits, and experimental techniques are described to ensure the successful digital implementation of closed-loop control of the buck power converter. The proper shielding of electrical wiring in power electronics allows improvement to the quality of the measures by removing noise induced by electromagnetic interference. A trigger signal is used to implement the Pulse-Width Modulation (PWM) with centered pulse and to synchronize the sampling of analogical signals from the buck converter. Such synchronization allows the use of a lower sampling frequency and ensures the measurements at the right instant in time. Experimental results are in good agreement with numerical simulations, showing the effectiveness of the control approach.

Several technological applications require well-designed control systems to induce a desired speed in direct current (DC) motors. Some controllers present saturation in the duty cycle, which generates variable switching frequency and subharmonics. The zero average dynamics and fixed point induction control (ZAD-FPIC) techniques have been shown to reduce these problems; however, little research has been done for DC motors, considering fixed switching frequency, quantization effects, and delays. Therefore, this paper presents the speed control of a DC motor by using a buck converter controlled with the ZAD-FPIC techniques. A fourth-order, non-linear mathematical model is used to describe the system dynamics, which combines electrical and electromechanical physical models. The dynamic response and non-linear system dynamics are studied for different scenarios where the control parameters are changed. Results show that the speed of the motor is successfully controlled when using ZAD-FPIC, with a non-saturated duty cycle presenting fixed switching frequency. Simulation and experimental tests show that the controlled system presents a good performance for different quantization levels, which makes it robust to the resolution for the measurement and type of sensor.

This paper presents a stability analysis of a buck converter using a Zero Average Dynamics (ZAD) controller and Fixed-Point Induction Control (FPIC) when the control parameter 𝑁, the reference voltage υref, and the source voltage 𝐸 are changed. The study was based on a previous analysis in which the control parameter was adjusted to 𝑁=1 and the parameter 𝐾𝑠 was changed during the simulation, finding the stability zone and regions with chaotic behavior. Thus, this new study presents the transient and steady-state behaviors and robustness of the buck converter when the control parameter 𝑁 changes. Moreover, numerical simulation results are compared with experimental observations. The results show that the system regulates the output voltage with low error when the voltage is changed in the source E. Besides, the voltage overshoot increases, and the settling time decreases when the control parameter 𝑁 is augmented and the control parameter 𝐾𝑠 is constant. Furthermore, the buck converter controlled by ZAD and FPIC techniques is effective in regulating the output voltage of the circuit even when there are two delay periods and voltage input disturbances.

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topic.