Home Energy Management System for Dynamic Loads using Mamdani Fuzzy Logic Approach

HEMS (home energy management systems) are controllers that manage and coordinate a home's generation, storage, and loads. These controllers are becoming increasingly important. To ensure that distributed energy penetration continues to grow resources are appropriately utilized and the process is not disrupted within the grid[1]. An approach to hems design based on behavioural control approaches is discussed in this paper which do not require accurate models or forecasts and are particularly responsive to changing situations, in this study. In this study, the role of the customer as well as the micro grid in intelligent demand management is demonstrated using MATLAB 2018 Fuzzy tool.[3]

A Hybrid Self-Voltage Balanced Multilevel Inverter Topologies for Induction Motors

A hybrid structured asymmetric switching capacitor multilevel inverter (ASC-MLI) is suggested in this work. The notion behind presenting this topology is to reduce the device count and DC sources as compared with conventional MLI. The step by step operating mode of single phase ASC-MLI is presented and by doing slight modifications the same configuration is used in three phase utility application and electric drive. The proposed configurations utilize major benefits of self-voltage balancing capability of capacitor voltage, which is independent from different load type and modulations index. To generate the switching pulse for corresponding switches the multi-carrier based sinusoidal pulse width modulation (MCS-PWM) technique is used; in addition to this simulation result are obtained using MATLAB/Simulink 2016b software version. Simulation results of an induction motor drive connected as three phase load highlights good performance of 17-level MLI.

Optimization of Phasor Measurement Unit (PMU) Placement: A Review

In today’s world, a Phasor Measurement Unit (PMU) is a crucial component of our power network for managing, controlling, and monitoring. PMU can provide synchronized voltage current, and frequency measurements in real time. We can't put a PMU in every bus in the electrical grid because it's not viable from a productivity and economic standpoint, and it's also not practical for handling huge data. As a result, it's critical to reduce the amount of PMU in the power network while also increasing the power network's observability. The optimal PMU placement problem is solved using a variety of methodologies. The paper's main goal is to provide a brief overview of synchrophasor technology, phasor measurement units (PMU), and optimal PMU placement in order to reduce the number of PMUs in the system while maintaining complete observability and application in today's power systems.

Kanban in Apparel Manufacturing: A Case Study

Pull system and Kanban are effective lean manufacturing tools. Though many companies in various manufacturing fields have successfully implemented pull manufacturing using Kanban systems, such studies in apparel manufacturing are rarely found. Kanban system was implemented in an apparel manufacturing company, which had implemented some of the lean manufacturing tools. The apparel manufacturing line was identified in four different sub sections and separate Kanbans were used in each sub section. The system was evaluated using two parameters: the number of Kanban cards in each point; the production output. Correct number of Kanban cards were found at all the time of the monitoring period of two weeks-the production efficiency has increased by 22%.

Real-Time Lime Quality Control through Process Automation

In the steel industry - Tata steel, India, most of the lime produced in the lime plant is used in the steel-making process at LD shops. The quality of steel produced at LD shops depends on the quality of lime used. Moreover, the lime also helps in the crucial dephosphorization process during steel-making. The calcined lime produced in the lime plant goes to the laboratory for testing its final quality (CaO%), which is very difficult to control. To predict, control and enhance the quality of lime during lime making process, five machine-learning-based models such as multivariate linear regression, support vector machine, decision tree, random forest and extreme gradient boosting have been developed using different algorithms. Python has been used as a tool to integrate the algorithms in the models. Each model has been trained on the past 14 months’ data of process parameters, collected from level 1 sensor devices, to predict the future quality of lime. To boost the model’s prediction performance, hyper-parameter tuning has been performed using grid-search algorithm. A comparative study has been done among all the models to select a final model with the least root mean square error in predicting and control future lime quality. After the comparison, results show that the model incorporating support vector machine algorithm has least value of root mean square error of 1.23 in predicting future lime quality. In addition to this, a self-learning approach has also been incorporated into support vector machine model to enhance its performance further in realtime. The result shows that the performance has been boosted from 85% strike-rate in +/-2 error range to 90% of strike-rate in +/-1 error range in real-time. Further, the above predictive model has been extended to build a control model which gives prescriptions as output to control the future quality of lime. For this purpose, a golden batch of good data has been fetched which has shown the best quality of lime (≥ 94% of CaO%). A good range of process parameters has been extracted in the form of upper control limit and lower control limit to tune the set-points and to give the prescriptions to the user. The integration of these two models (Predictive model and control model) helps in controlling the quality of lime 12 hours before its final production of lime in lime plant. Results show that both models (Predictive model and control model) have 90% of strike-rate within +/-1 of error in real-time. Finally, a human machine interface has been developed to facilitate the user to take action based on control model’s output. Eventually this work is deployed as a lime making process automation to predict and control the lime quality.

Fake Product Monitoring and Removal for Genuine Product Feedback

A customer’s decision to purchase a product or service are primarily influenced by online reviews. Customers use online reviews, which are valuable sources of information to understand the public opinion on products and/or services. Dependability on online reviews can give rise to the potential concern that violator could give deceitful reviews in order to synthetically promote or decry products and services. This practice is known as Opinion Spam, where spammers manipulate reviews by making fake, untruthful, or deceptive reviews to get profit and boost their products, and devalue a competitor’s products. In order to tackle this issue, we propose to build a fraud risk management system and removal model. This captures fraudulent transactions based on user behaviors and network, analyses them in real-time using Data Mining, and accurately predicts the suspicious users and transactions. In this system, we use two algorithms NLP and TF-IDF to differentiate between fake and genuine reviews or feedback received by the customers

Deconstructivism in Philosophy, Architecture, and Reusability Contribution

Interest in philosophy and the humanities increased after the Second World War, especially in the West, as the critical movement began to reconsider the Western intellectual and philosophical heritage, and emerged approaches known as” postmodernism“, as critical foundations of Western cultural thought, and a product of that cultural and cognitive movement known as” postmodernism"; the concept of postmodernism, which is central This term is associated with a very diverse group that is rarely associated with each other with common things. The idea of postmodernism appears in a critique of the literary and philosophical trends on which modernism was based, and some theorists and philosophers believe that postmodernism is closely related to the social and political transformations that took place in industrial societies such as the postindustrial or knowledge society. Deconstruction is important critical movement as well as controversial, and no theory in literary criticism has provoked waves of admiration and created a state of aversion and resentment as well, as deconstruction has done in contemporary literary thought. Deconstruction emerged in the 1960s as a reaction to structuralism, the dominance of language, the centralization of the mind, and the dominance of linguistics over all fields of knowledge, and from the 1970s deconstruction became a literary critical methodology, and a mechanism for evaluating rhetoric and interpretation. Deconstructionism is mainly a critique of the structural proposition, which has been working to reveal the basic structures responsible for the most noticeable features of social and cultural interaction, since deconstructionism consistently negates the meaning in the text system and analyzes the margins, gaps, expectations, contradictions and conclusions within the texts, as formulations that contribute to the disclosure of the background of language and structure.