Mohammed Mujahid Irfan
@sru.edu.in
Assistant Professor
SR UNIVERSITY
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Control and Systems Engineering, Engineering, Multidisciplinary
Scopus Publications
Scopus Publications
B. Latha, Mohammed Mujahid Irfan, Aymen Flah, Vojtech Blazek, Lukas Prokop, and Shriram S. Rangarajan
Elsevier BV
Latha Bachhati, Mohammad Mujahid Irfan, and Butukuri Koti Reddy
Elsevier BV
Mujahid Irfan, Sudhakar Angatha, Shriram Rangarajan, Sruthi Nookala, Mahesh Ontera, Bhavan Thummala, Suresh Badhavath, Chandu Prasad, Nandam, and Sunil Page
AIP Publishing
Md Mujahid Irfan, P. Chandrasekhar, and M. Sushama
Elsevier BV
Mohammad Mujahid Irfan, Sushama Malaji, Chandrashekhar Patsa, Shriram S. Rangarajan, and S. M. Suhail Hussain
MDPI AG
Green energy sources are implemented for the generation of power due to their substantial advantages. Wind generation is the best among renewable options for power generation. Generally, the wind system is directly connected with the power network for supplying power. In direct connection, there is an issue of managing power quality (PQ) concerns such as voltage sag, swells, flickers, harmonics, etc. In order to enhance the PQ in a power network with a wind energy conversion system (WECS), peripheral compensation is needed. In this paper, we highlight a novel control technique to improve the PQ in WECS by adopting an Artificial Neural Network (ANN)-based Distribution Static Compensator (DSTATCOM). In our proposed approach, an online learning-based ANN Back Propagation (BP) model is used to generate the gate pulses of the DSTATCOM, which mitigate the harmonics at the grid side. It is modelled using the MATLAB platform and the total harmonic distortion (THD) of the system is compared with and without DSTATCOM. The harmonics at the source side decreased to less than 5% and are within the IEEE limits. The results obtained reveal that the proposed online learning-based ANN-BP is superior in nature.
Md Mujahid Irfan, D. Ravi Teja, K. Devender Reddy, and V. Neeraj Kumar
AIP Publishing
MD Mujahid Irfan, M. Sushama, and P. Chandrasekhar
AIP Publishing
MD Mujahid Irfan, M. Sushama, and P. Chandrasekhar
AIP Publishing
P. Sucharitha, E. Swarnalatha, K. Sravan Kumar, and MD Mujahid Irfan
AIP Publishing
MD Mujahid Irfan, Pothu Dheeraj, Gajula Rohith Raj, Khaja Misbah Uddin, and Shriram S. Rangarajan
AIP Publishing
Abhinav Rampeesa, Ponnaboina Akhila, Mohammed Irfan, Shashank Rebelli, Laxman Raju Thoutam, and J. Ajayan
IEEE
The majority of the most recent data-driven advancements, notably artificial-intelligence(AI) and machine-learning (ML) depend heavily on binary arithmetic computations. This paper focuses on the design and analysis of a reliable, low-power 4-bit Baugh-Wooley (BW) multiplier employing the high performance 1-bit mirror full-adder (MFA) and approximate full-adder (AFA). For a various technological nodes varying from 16 nm to 90 nm, the effectiveness of the proposed 4-bit BW multiplier is thoroughly investigated for power and delay parameters at different operating voltages (0.6 V – 1.0 V). The proposed 4-bit BW multiplier at a 16-nm CMOS process employing an MFA circuit consumes a minimal power of 37.5 μW at an operating voltage of 0.7 V and a nominal temperature of 300C, whereas it consumes 35.5 μW for a combined MFA and AFA circuits. The power consumption increases linearly with operating voltage for the designed BW multiplier employing the two high performance full-adder circuits. The proposed 4-bit BW multiplier at 16-nm CMOS process has a delay of 104 ps at 0.7 V. The simulation result analysis indicates the combination of MFA and AFA circuits in the design of low-power 4-bit Baugh-Wooley multipliers, even though there exists a partial error at the output of multiplier MSB.
Md Mujahid Irfan, M. Sushama, and P. Chandrasekhar
Springer Nature Singapore
Mohammad Mujahid Irfan, Sushama Malaji, Chandarashekhar Patsa, Shriram S. Rangarajan, Randolph E. Collins, and Tomonobu Senjyu
MDPI AG
The technology transformation of industry 4.0 comprises computers, power converters such as variable speed devices, and microprocessors, which distract from the quality of power. The integration of distribution-generation technologies, such as solar photovoltaic (PV) and wind systems with source grids, frequently uses power converters, which increases the issues with power quality. DSTATCOM is the FACTS device most proficient in recompensing current-related power quality concerns. A model of DSTATCOM with an ANN controller was developed and implemented using a backpropagation online learning-based algorithm for balanced non-linear loads. This algorithm minimized the mathematical burden and the complications of control. It demonstrated a dynamic role in improving the quality of the power at the grid. The algorithm was implemented in MATLAB using an ANN model controller and the results were validated with an experimental set-up using an FPGA controller.
Md Mujahid Irfan, Shriram S. Rangarajan, E. Randolph Collins, and Tomonobu Senjyu
MDPI AG
Grid interactive solar photovoltaic (PV) and electric vehicle (EV) systems are the emerging technologies nowadays, mainly due to energy cost reduction and minimization of emission levels. Various research surveys have presented the effect of grid integration of PVs and EVs in an isolated way. However, it is worth accepting that with the continuous emergence of PVs and EVs, the power grid is experiencing the combined effect of PV–EV integration. The distribution system network of EVs impacts the power quality of the grid. In this paper, shunt active power filter is modelled using neuro-fuzzy control technique for the mitigation of harmonics using MATLAB. The improvement in the system performance is analyzed and compared with the traditional compensation techniques.