Recent Advances in Electrical & Electronic Engineering - Volume 15, Issue 4, 2022

Background: Currently, electricity producers are mainly concerned with finding solutions that make it possible to produce electricity with a low fuel cost and minimize toxic gas emissions into the atmosphere. Objective: This paper aims to demonstrate the economic and environmental impact of connecting photovoltaic plants on the electricity grid to reduce the cost of electricity production and the emissions of toxic gas into the atmosphere. Methods: The study was carried out under the MATLAB environment using the PSO algorithm (Particle Swarm Optimizers), which makes it possible to optimize the cost function and the gas emissions function in two cases: In the first case, the electricity network is considered without photovoltaic generation; and in the second case with the addition of photovoltaic generation. Results: After connecting the photovoltaic plant to the electricity network, we obtained a remarkable reduction in the cost of production and the quantity of emission of toxic gases. Conclusion: The economic and environmental impact of the photovoltaic plant on the electricity grid has been well demonstrated by applying the method of particle swarm optimization (PSO). We compared the results obtained before and after connecting the photovoltaic plant to the electricity grid; we obtained good results in terms of reduction in production costs and greenhouse gas emissions.

Background: Because of significant challenges in locating conventional drivers into luminaires, and also cost issues, a noticeable attitude toward AC direct LED drivers has been shaped. Nevertheless, the multiplicity of target parameters like output lumen, efficacy in lumen per watt, power consumption, PF, THD, and so on limits designers to select an optimal design of LED modules applying AC direct LED drivers. Methods: The most crucial issue in the design phase is selecting the Optimal Number of LEDs in Current Channels (ONLCC) fed by the AC direct driver. The main contribution of the paper is utilizing an optimization method based on a stochastic search to optimize the aforementioned parameters. Results: The parameters to be optimized are inserted into the predefined objective functions. Different case studies conducted in the presented work show that the proposed method finds the optimal parameters while the objective function is minimized. Such a paradigm can save time spent in the design phase. Furthermore, a stability study is conducted to explore the stochastic search capability in solving the ONLCC problem. Conclusion: The obtained results from the simulation are supported by practical tests. All the validation tests in practice are conducted in the electro-optics laboratory of the Mazinoor lighting industry.

Background: Electricity consumption forecast is an important basis for the power system to achieve regional electricity balance and electricity spot market transactions. Objective: In view of the fact that many electricity consumption prediction models do not make good use of the correlation of data in the time dimension and space dimension, this paper proposes a day-ahead forecasting model based on spatiotemporal correction, which further improves the forecasting accuracy of electricity demand. Methods: Firstly, the long short-term memory (LSTM) model is used to construct the forecasting model. Secondly, from the perspectives of time correlation and space correlation, meanwhile considering calendar factors and meteorological factors, the K-Nearest Neighbors (KNN) model is taken to construct correction models, which can correct the forecasting results of LSTM. Results: According to the analysis of power consumption data of 9 areas in New England, the mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) of time dimension correction model are reduced by 0.35%, 5.87% and 5.06%, and the 3 evaluation metrics in space dimension are decreased by 0.52%, 6.82% and 7.06% on average. Conclusion: The results prove that the models proposed in this paper are effective.

Aims: This paper describes the process of spotting a barrier on the path of a wheeled robot to find an alternate way of avoiding it. Methods: For path planning, an image-processing technique is used, and for the barrier-sensing and avoidance system, an ultrasonic sensor is used. FPGA board processes the sensor output, and after processing, it instructs the motor arrangement to control the speed and direction of the wheeled robot. Results: We took one digital storage oscilloscope to collect the data from the sensor board. Although the color and lumen of the reflection matter a lot in the reading, various objects were selected to reduce the probability of error. A Very High-Speed Integrated Circuit (VHSIC) Hardware Description Language (VHDL) block is generated to control the algorithm and is connected with an enabled pin. The calculation block and the ADC have been illustrated. As the FPGA board reads a digital signal, we converted the received signal to digital with the help of an ADC of type NS ADC128S022. It is a 12-bit converter. Initially, we kept bigger particles in front of the sensor to understand and quantify its highest scope. DSO is used to view the response of both the pulse and the analog signal. The code, which we have written for the operation of the ultrasonic sensor, used a high logic of 10 μs width for triggering the input. The program measures the object distance by calculating the logic high time of the ECHO pin we have programmed as input. For servo operation, a pulse was detected in 1.5ms over a 20-millisecond time-period. To change the rotating angle, we adopted different on time. Conclusion: Wheeled robots have a number of applications. Depending on what sensors, actuators or microprocessors are applied, the response time differs from one model to the other. A robot should respond very quickly to the signals in real-time applications. In a realistic approach, the FPGA development board has been widely used in robot models because of its fast response time, flexible system approach, quick generation of signals, parallel processing ability, and very low cost compared to other processors.

Background: The single position control or force control of the electro-hydraulic servo system has long been unable to meet the actual needs of production and life. To achieve a good control effect and solve various problems in real life, people no longer only consider a single position control or force control. Objective: The proposed method provides technical support for multi-objective collaborative control of the electro-hydraulic servo system, completes the requirements of multitasking operation, improves the positioning accuracy and response velocity of the electro-hydraulic servo system, and realizes the synergy between the position and force. Methods: A control method of the outer loop control of force as feedforward compensation is adopted to realize the coordinated control of force and position based on position control. The position control part adopts the PID control algorithm to ensure real-time response accuracy and positioning accuracy. The outer force loop uses an adaptive fuzzy neural network control algorithm to reduce vibration and shock caused by system instability and nonlinear factors during the force control process. Results: The control effect of the designed position/force collaborative controller is verified by simulation and experiment. It can effectively reduce the vibration and impact caused by the contact force during the working process of the system, at the same time greatly improving the accuracy of position control and improving the stability of the system. Conclusion: The designed force/position collaborative control system has a good overall control effect.

This paper presents a novel single-sided linear actuator design and sensitivity analysis of different geometric parameters on the performances of the actuator. A numerical model based on the finite element method is developed for that purpose. Thus, the electromagnetic behavior of the actuator is analyzed with 2D magneto-static analysis, and the electromagnetic characteristics were presented with the study of the effect of different geometric parameters on the thrust force. Indeed, the magnetic flux distribution and the force profile are presented and discussed. The analysis of different parameters on the actuator's performance allows the selection of the appropriate values of these parameters to obtain optimal performance of the actuator. The analysis shows the air gap and stack length has a powerful influence on the actuator's performance. Furthermore, the increase of supply current has a significant effect, but a high level of Ampere turns to bring the magnetic circuit to the saturation zone. It is important to determine the optimal geometrical dimensions to reduce the mass of the motor. It is critical to choose the optimum power values for the actuator to obtain optimum performance and avoid exceeding operating limits. Background: The linear switched reluctance motor is used in many industrial applications such as the linear motion of machine tools, sliding door applications, and conveyors. Objective: A new design approach for a linear switched reluctance motor is developed in order to determine the geometric parameters of the structure. Methods: Finite element method is used to predict the characteristics of the motor and the analysis its performances. Results: The simulation results obtained by finite element analysis make it possible to study the electromagnetic behavior of the motor and to validate the established geometric model. Conclusion: The study was carried out to choose the optimal parameters in order to obtain optimal performance and avoid exceeding the operating limits of the motor.

Background: With the rapid development of science, more data is available to human beings. Therefore, the storage and calculation of big data have become the focus of scientific research. MapReduce performs well in the big data processing. However, it is prone to data skew, which affects the overall efficiency of the data processing cluster. Objective: Aiming at the low efficiency of MapReduce data join, this paper proposes an intelligent data join load balancing algorithm based on dynamic programming. The algorithm introduces data sampling and partition algorithms. Due to the high performance of dynamic programming in the data constraint problem, it is used to solve the data skew problem intelligently. Methods: Firstly, the causes of data skew are analyzed and the data partition method is improved. The algorithm introduces a data sampling method. In the task allocation stage, the multidimensional knapsack algorithm is used. Different key values are evenly divided to each computing node through the load cost. Finally, The performance of the improved algorithm is verified by experiments. Results: The experimental results show that compared with the traditional load balancing algorithm and the existing improved algorithm, the new algorithm improves the data processing efficiency, reduces the data skew problem and better solves the problem of data load imbalance. Conclusion: A two-table equivalent join load balancing algorithm based on key cost has been proposed. The algorithm creatively combines dynamic programming with intelligent data sampling, which greatly improves the efficiency and quality of data processing. The algorithm is worthy of popularization and application.