Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

Consistency Checking of the IEC 61508 PFH Formulas and New Formulas Proposal Based on the Markovian Approach

Safety Instrumented Systems (SISs) are of prime importance in protecting people, assets and environment from hazardous events. Therefore, it is important to be able to assess accurately their performance indicators. For this end, IEC 61508 standard has provided two reliability metrics: the average failure probability of a SIS lowly demanded (PFDavg) and the average failure frequency of a SIS highly or continuously demanded (PFH). The aim of this paper is to investigate the IEC 61508 PFH formulas and to propose new ones based on the Markovian approach. Indeed, the new edition of IEC 61508 provides PFH formulas reflecting the possibility of automatic shutdown of the monitored process upon detection of a dangerous failure in the SIS. However, the IEC 61508 attempt remains incomplete and provide non-conservative results, which is dangerous from a safety point of view.

Optimal Fractional-Order PI Control Design for a Variable Speed PMSG-Based Wind Turbine

This paper focusses on the design of optimal control strategies for a variable-speed wind energy system based on Permanent Magnet Synchronous Generator (PMSG). The fractional order PI controller, denoted PIλ, is an extension of the classical PI controller, which provides greater flexibility, better performance and robustness, however the tuning of the controller parameters is challenging. In this work, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) provide approximate solutions to various problems and form a good optimization. In our system, they are used to have the PI regulator parameters and tune the parameters of the proposed controllers. The proposed controllers have been applied as maximum power point (MPPT) controllers for the wind turbine and to regulate the PMGS currents under variable weather conditions and. The results show that, among all these controllers, the fractional order PI controller optimized by the PSO leads to better performance in terms of the transient response characteristics such overshoot, rise time and settling time.

Real-Time High Performance of Induction Motor Drive Using Hybrid Fuzzy-Sliding Mode Controllers

Obtaining the required performance, stability, and robustness in real-time control of induction motors usually requires the use of complex controllers, however through multiple experimentations, many challenges have arisen from such methods. The complex structure of control methods in real-time applications is usually computationally challenging and energy consuming, hence the need for a simple control strategy to overcome these challenges, in this paper, we focus on designing an advanced hybrid control strategy with a simple design applied to an induction motor. Mainly, the hybrid controller used in this study has the benefits of joining the best performance of both fuzzy logic controller and sliding mode controller, specifically designed to handle each phase separately, the transition phase and the steady phase. A fuzzy controller intervenes as a supervisor in our control structure, more specifically it manages the switch from one type of control to the other taking into account the intervention phase of each type of controller by commanding the rate of both controllers. Control performance analysis was carried out in a real experimental setup to validate the efficiency and robustness of the proposed hybrid controller and confirm its effectiveness in handling the compromise between overshoot and response time.

Discrete Event Systems Fault’s Diagnosis and Prognosis Using Feed-Forward Neural Networks

The elaboration and development of monitoring (diagnostic and prognostic) tools for industrial systems has been one of the main concerns of the researchers for many years, so that many researches and studies have been developed and proposed, especially concerning discrete event systems (DES), which occupy an important class of industrial systems. However, the use of modeling tools to ensure these operations become a complex and exhausting task, while the complexity of industrial systems has been increasing incessantly. Therefore, the development of more and more sophisticated techniques is required. In this context, the use of artificial neural networks (NN) seems interesting, because thanks to their automatics and intelligent algorithms, the NN could handle perfectly DES diagnosis and prognosis problems. For this purpose, in the following papers, we propose an intelligent approach based on feed-forward neural network, which will deal with fault diagnosis and prognosis in DES, so that the events generated by the DES, will be presented and analyzed by the neural network in real-time, in order to perform an online diagnosis and prognosis.

Performance Evaluation of PV Panel Configurations Considering PSC’s for PV Standalone Applications

One of the major concerns for continuous solar photovoltaic (PV) generation is partial shading. The movement of clouds, shadow of buildings, trees, birds, litter and dust, etc., can lead to partial shadow conditions (PSCs). The PSCs have caused inconsistent power losses in the PV modules. This leads to a shortage of electricity production and the presence in the PV curve of several peaks. One of the simplest solutions to PSC’s is the PV configurations. The objective of this paper is modelling and simulation of solar PV system in various shading scenarios for KC200GT 200 W, 5 x 5 configurations that includes Series/Parallel (SP), Total-Cross-Tied (TCT), Triple-Tied (TT), Bridge-Link (BL) configurations. Real time PSC’s such as corner, center, frame, random, diagonal, right side end shading conditions are evaluated under all PV array configurations. A comparative analysis is carried out for the parameters such as open circuit voltage, short circuit current, maximum power point, panel mismatch losses, fill factor, efficiency under all PV configurations considering PSC’s. From the comparison analysis best configuration will be presented.

Reduce Carbon Emissions of Logistic Transportation Using Eight Steps Approach in Indonesian Automotive Industry

The global competition encourages Indonesia to advance the economy, especially in manufacturing by implementing sustainable manufacturing. Companies must consider transportation costs and concern for the environment due to the large increase in greenhouse gas emissions and the increase in NOx, Particulate, and various other harmful pollutants. Emissions from transportation activities cause global climate change and damage air quality and human health in regional and urban areas. At the same time, the movement of empty containers can result in air pollution due to CO2 emissions which have a negative impact on sustainable development. This study aims to reduce carbon emissions in the logistics transportation chain in the Automotive Manufacturing Industry. The method used is the Eight Step Approach. The method used is systematic and structured from defining the problem to standardizing improvements. Analysis of the causes of the problem and proposed improvements are determined by Focus Group Discussion (FGD) with expert judgment. The source of the data obtained comes from field observations, FGD, company reports from 2019 to 2021. This research has proven that reducing carbon emissions has an impact on company profits. The largest decrease was contributed by improvements in transportation routes. The ratio of reducing carbon emissions by 2020 is 2.6% or an increase in efficiency compared to the previous year.

Metaheuristic Optimization of PD and PID Controllers for Robotic Manipulators

In this paper, the Particle Swarm Optimization algorithm (PSO) is combined with Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) to design more efficient PD and PID controllers for robotic manipulators. PSO is used to optimize the controller parameters Kp (proportional gain), Ki (integral gain) and Kd (derivative gain) to achieve better performances. The proposed algorithm is performed in two steps: (1) First, PD and PID parameters are offline optimized by the PSO algorithm. (2) Second, the obtained optimal parameters are fed in the online control loop. Stability of the proposed scheme is established using Lyapunov stability theorem, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. Computer simulations of a two-link robotic manipulator have been performed to study the efficiency of the proposed method. Simulations and comparisons with genetic algorithms show that the results are very encouraging and achieve good performances.