Sukumar Mishra

Bio: Sukumar Mishra is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Photovoltaic system & Microgrid. The author has an hindex of 44, co-authored 405 publications receiving 7905 citations. Previous affiliations of Sukumar Mishra include University College of Engineering & Indian Institutes of Technology.

Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network

Abstract: This paper presents an S-Transform based probabilistic neural network (PNN) classifier for recognition of power quality (PQ) disturbances. The proposed method requires less number of features as compared to wavelet based approach for the identification of PQ events. The features extracted through the S-Transform are trained by a PNN for automatic classification of the PQ events. Since the proposed methodology can reduce the features of the disturbance signal to a great extent without losing its original property, less memory space and learning PNN time are required for classification. Eleven types of disturbances are considered for the classification problem. The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events. The classification performance of PNN is compared with a feedforward multilayer (FFML) neural network (NN) and learning vector quantization (LVQ) NN. It is found that the classification performance of PNN is better than both FFML and LVQ.

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

Abstract: Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand–supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control

Abstract: A maiden attempt is made to examine and highlight the effective application of bacterial foraging (BF) to optimize several important parameters in automatic generation control (AGC) of interconnected three unequal area thermal systems, such as integral controller gains (KIi) for the secondary control, governor speed regulation parameters (Ri) for the primary control and frequency bias parameters (Bi), and compare its performance to establish its superiority over genetic algorithm (GA) and classical methods. Comparison of convergence characteristics of BF, GA, and classical approach reveals that the BF algorithm is quite faster in optimization, leading to reduction in computational burden and giving rise to minimal computer resource utilization. Simultaneous optimization of KIi, Ri, and Bi parameters which surprisingly has never been attempted in the past, provides not only best dynamic response for the system but also allows use of much higher values of Ri (than used in practice), that will appeal to the power industries for easier and cheaper realization of governor. Sensitivity analysis is carried out which demonstrates the robustness of the optimized KIi, Ri, and Bi to wide changes in inertia constant (H), reheat time constant (Tr), reheat coefficient (Kr), system loading condition, and size and position of step load perturbation.

Permanent Magnet Synchronous Generator-Based Standalone Wind Energy Supply System

Abstract: In this paper, a novel algorithm, based on dc link voltage, is proposed for effective energy management of a standalone permanent magnet synchronous generator (PMSG)-based variable speed wind energy conversion system consisting of battery, fuel cell, and dump load (i.e., electrolyzer). Moreover, by maintaining the dc link voltage at its reference value, the output ac voltage of the inverter can be kept constant irrespective of variations in the wind speed and load. An effective control technique for the inverter, based on the pulsewidth modulation (PWM) scheme, has been developed to make the line voltages at the point of common coupling (PCC) balanced when the load is unbalanced. Similarly, a proper control of battery current through dc-dc converter has been carried out to reduce the electrical torque pulsation of the PMSG under an unbalanced load scenario. Based on extensive simulation results using MATLAB/SIMULINK, it has been established that the performance of the controllers both in transient as well as in steady state is quite satisfactory and it can also maintain maximum power point tracking.

Differential Evolution: A Survey of the State-of-the-Art

Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

Ultracapacitors: Why, How, and Where is the Technology

Abstract: The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Energy function analysis for power system stability

Abstract: (1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.