Geetha Mani

Bio: Geetha Mani is an academic researcher from VIT University. The author has contributed to research in topics: Control theory & PID controller. The author has an hindex of 3, co-authored 25 publications receiving 50 citations. Previous affiliations of Geetha Mani include PSG College of Technology.

Intuitionistic Fuzzy Expert System based Fault Diagnosis using Dissolved Gas Analysis for Power Transformer

Abstract: In transformer fault diagnosis, dissolved gas analysis (DGA) is been widely employed for a long period and numerous methods have been innovated to interpret its results. Still in some cases it fails to identify the corresponding faults. Due to the limitation of training data and non-linearity, the estimation of key-gas ratio in the transformer oil becomes more complicated. This paper presents Intuitionistic Fuzzy expert System (IFS) to diagnose several faults in a transformer. This revised approach is well suitable to diagnosis the transformer faults and the corresponding action to be taken. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.

Fuzzy Logic Control for Solar PV Fed Modular Multilevel Inverter Towards Marine Water Pumping Applications

Abstract: This paper presents the design and implementation of Modular Multilevel Inverter (MMI) to control the Induction Motor (IM) drive using intelligent techniques towards marine water pumping applications. The proposed inverter is of eleven levels and has the ability to control the speed of an IM drive which is fed from solar photovoltaics. It is estimated that the energy consumed by pumping schemes in an onboard ship is nearly 50% of the total energy. Considering this fact, this paper investigates and validates the proposed control design with reduced complexity intended for marine water pumping system employing an induction motor (IM) drive and MMI. The analysis of inverter is carried out with Proportional-Integral (PI) and Fuzzy Logic (FL) based controllers for improving the performance. A comparative analysis has been made with respect to better robustness in terms of peak overshoot, settling time of the controller and Total Harmonic Distortion (THD) of the inverter. Simulations are undertaken in MATLAB/Simulink and the detailed experimental implementation is conducted with Field Programmable Gate Array (FPGA). The results thus obtained are utilized to analyze the controller performance, improved inverter output voltage, reliable induction motor speed control and power quality improvement by reduction of harmonics. The novelty of the proposed control scheme is the design and integration of MMI, IM drive and intelligent controller exclusively for marine water pumping applications.

Investigation and validation of solar photovoltaic-fed modular multilevel inverter for marine water-pumping applications

Abstract: This article discussed the development of a solar photovoltaic-fed modular multilevel inverter (MMI) with reduced switch count to operate an asynchronous motor drive for maritime applications. The proposed marine water-pumping system consist of a PV panel, an asynchronous motor drive, and modular inverter. The suggested topology can produce 11 levels of output using asymmetric DC sources. The proposed MMI consists of five DC sources, and they are powered by the PV panels. The primary advantage of the proposed topology is that it does not need any auxiliary circuit to produce the negative levels. Moreover, the active sources (PV panels) in the proposed system are reduced by implementing a modified single-input and multiple-output SEPIC converter. The power consumption by on-board pumping systems in maritime is estimated to be almost 50% of the total power. Taking this into account, this article investigates an adaptive fuzzy logic-based closed-loop control design for reducing the losses in the induction machine and thereby improving the efficiency of the system. Further, the performance of the proposed system is compared with the conventional PI controller, and from the results, it is proved that the proposed control system works effectively in reducing the losses as well as improving the efficiency of the system. The simulations are carried out in MATLAB/Simulink, and the experimental investigations are carried out in the laboratory. The obtained experimental results are similar to the simulation results.

Energy Management and Control for Grid Connected Hybrid Energy Storage System Under Different Operating Modes

Abstract: DC-coupled microgrids are simple as they do not require any synchronization when integrating different distributed energy generations. However, the control and energy management strategy between the renewable energy sources and the energy storages under different operating modes is a challenging task. In this paper, a new energy management scheme is proposed for the grid connected hybrid energy storage with the battery and the supercapacitor under different operating modes. The main advantages of the proposed energy management scheme are effective power sharing between the different energy storage systems, faster dc link voltage regulation to generation and load disturbances, dynamic power sharing between the battery and the grid based on the battery state of charge, reduced rate of charge/discharge of battery current during steady state and transient power fluctuations, improved power quality features in ac grid and seamless mode transitions. The effectiveness of the proposed method is validated by both simulation and experimental studies.

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Abstract: Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

Research on multi-attribute decision-making in condition evaluation for power transformer using fuzzy AHP and modified weighted averaging combination

Abstract: Power transformers are of vital importance for the stable operation of power system. Hence, to grasp transformers’ health condition is of imminent importance. However, the evaluation of transformers’ health condition is bound with fuzzy, uncertainty, and even conflict information. To address these problems, a novel evaluation model for transformer, by integrating the merits of fuzzy set theory, fuzzy analytical hierarchical process (AHP), and modified weighted averaging combination, is proposed in this study. The evaluation model contains four factors: dissolved gas analysis, electrical testing, oil testing, and miscellaneous factors, as well as 20 indices. The evaluation process consists of three steps. First, the fuzzy set theory is used to obtain assessment grades for each quantified indices. Second, the fuzzy AHP using fuzzy preference programming, which extends classical AHP and can better tackle the uncertainty existed in the comparison matrix given by experts, is introduced to calculate the weights of indices. Finally, the assessment is processed through the modified weighted averaging combination, which is able to handle the conflicts among evidences. The proposed method is verified by evaluating a realistic transformer, and the results of field examples indicate that the method can evaluate the transformer health condition effectively.

Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm

Abstract: Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.

Investigations of Multi-Carrier Pulse Width Modulation Schemes for Diode Free Neutral Point Clamped Multilevel Inverters

Abstract: Multilevel Inverters (MLIs) are widely used in medium voltage applications due to their various advantages. In addition, there are numerous types of MLIs for such applications. However, the diode-less 3-level (3L) T-type Neutral Point Clamped (NPC) MLI is the most advantageous due to its low conduction losses and high potential efficiency. The power circuit of a 3L T-type NPC is derived by the conventional two level inverter by a slight modification. In order to explore the MLI performance for various Pulse Width Modulation (PWM) schemes, this paper examines the operation of a 3L (five level line to line) T-type NPC MLI for various types of Multi-Carriers Pulse Width Modulation (MCPWM) schemes. These PWM schemes are compared in terms of their voltage profile, total harmonic distortion (THD) and conduction losses. In addition, a 3L T-type NPC MLI is also compared with the conventional NPC in terms of number of switches, clamping diodes, main diodes and capacitors. Moreover, the capacitor-balancing problem is also investigated using the Neutral Point Fluctuation (NPF) method with all of the MCPWM schemes. A 1kW 3L T-type NPC MLI is simulated in MATLAB/Simulink and implemented experimentally and its performance is tested with a 1HP induction motor. The results indicate that the 3L T-type NPC MLI has better performance than conventional NPC MLIs.