Browsing by Author "Kabir, M.A.,"

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An approach to imbalance power management using demand response in electricity balancing market
(Institute of Electrical and Electronics Engineers Inc., 2019-09) Kabir, M.A.,; Sunny, M.R.,; Zhang, C.
Imbalance power management is a key operational task for a system operator. Imbalance power is produced from the difference in electrical energy supply and demand in the real operational time which deviates power system stability. In this paper, a balancing market model using demand response is developed to mitigate imbalance power. In context, a case study of balancing market model is investigated using flexible residential load along with nord pool spot market and imbalance data Therefore, the market model is simulated in MATLAB for five weeks. The simulation result is evaluated in order to determine imbalance reduction value, flexibility benefits and demand response value. Finally, the study is concluded with the propositions of future balancing market model contribution in imbalance reduction using demand response.
Proposed PV transformer-less inverter topology technique for leakage current reduction
(International Journal of Power Electronics and Drive Systems, 2016-09) Khan, M.N.H.,; Delwar, M.H.; Kabir, M.A.,; Zahan, M.S.,; Ahmad, K.J; Alam, M.M.; Anower, M.T.
Importance and demand of using renewable energy is dramatically escalated globally. Hence, the use of renewable energy is going to touch in peak. This demand is varying according to the site choosing. For instance, Wind is preferable where air is following highly as well as solar recommended place is high sun ray reducing places. Especially, the renewable system is highly recommended for electrification issues where it’s possible to produce the electricity for fulfilling rural and remote areas electricity problem. The photovoltaic (PV) panel of connecting with transformer based system is popular where some limitations are occurred especially cost and weight. In contrast, in this paper is focusing these issues where the transformer-less inverter system is used. Here will discuss some transformer-based and transformer-less inverter topologies and the leakage current issue which is occurred when transformer-less inverter system is used. Moreover, here is proposed a topology for reducing the leakage current after doing switching technique in both 50% and 75% duty cycle where output voltage remains quite same. © 2016 Institute of Advanced Engineering and Science. All rights reserved.
Residential Energy Management: A Machine Learning Perspective
(IEEE Computer Society, 2020) Sunny, M.R.,; Kabir, M.A.,; Naheen, I.T.,; Ahad, M.T.
In smart grids, residential energy management is a vital part of demand-side management. It plays a pivotal role in improving the efficiency and sustainability of the power system. However, challenges such as variability of consumption profiles require machine learning to understand and forecast residential demands. Moreover, machine learning based intelligent load management is required for effective implementation of demand response programs. In this article, applications of machine learning algorithms in residential demand forecasting, load profiling, consumer characterization, and load management are comprehensively discussed. The article also examines the characteristics and availability of relevant databases, and explores research challenges and possibilities.
Smart Meter Data Compression and Load Profile Classification using UMAP and Random Forest
(Institute of Electrical and Electronics Engineers Inc., 2021) Sunny, M.R.,; Kabir, M.A.,; Islam, R.,; Nazifa, S.
In this paper, Uniform Manifold Approximation and Projection (UMAP) is used to compress electricity consumption data. The Random Forest (RF) classification algorithm is then used on the compressed data to learn the consumption patterns of two distinctive user base - household consumers and SMEs (small and medium businesses). Compression ratio achieved by UMAP and classification accuracy of our classifier model are compared with conventional methods and various machine learning pipelines proposed in recent studies. The results demonstrate that our proposed technique achieves better compression ratio and classification accuracy compared to the conventional methods.