The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

ECG Biometric Recognition: A Comparative Analysis

Dissolved gas analysis (DGA) is used to assess the condition of power transformers. It uses the concentrations of various gases dissolved in the transformer oil due to decomposition of the oil and paper insulation. DGA has gained worldwide acceptance as a method for the detection of incipient faults in transformers.

A review of dissolved gas analysis measurement and interpretation techniques

Grey wolf optimization applied to economic load dispatch problems

Application of computational intelligence techniques for load shedding in power systems: A review

This paper presents a review on the sources of failures of transformer in the substation. Different investigations and test analyses have been conducted to identify the root causes of failure of the transformer in the power system, and to identify the preventive measures to avoid these breakdowns. The review work has been presented with the focus on bushing-failure, which has been identified as one of the major causes of transformer breakdown in different reported studies. In determining the lifespan of the insulation of the high voltage bushing, along with different factors, moisture effect has also been considered in presenting the degradation analysis. On-line measurement has been preferred over off-line measurements due to its benefits measuring the reliability and uninterrupted functioning of the future generation, transmission and distribution systems. Post fire-outbreak, external inspection of the damaged bushing along with analytical results, obtained with the aid of Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) analysis, have also been included in this study. In the analysis, electric field and equipotential lines with different oil levels have been simulated using COMSOL Multiphysics software.

Causes of transformer failures and diagnostic methods – A review

A survey of emerging biometric modalities

Power transformer is vital equipment in any electrical power system. So any fault in the power transformer may lead to the interruption of power supply and accordingly the financial losses will also be great. So it is important to detect the incipient faults of transformer as early as possible. Among the existing methods for identifying the incipient faults, dissolved gas analysis (DGA) is the most popular and successful method. Any kind of fault inside transformer gives rise to overheating and will produce characteristics amount of gases in transformer oil. In this paper classical methods of DGA such as Key Gas Method, Rogers Ratio Method and Duval Triangle Method are reviewed first and the need to integrate with the artificial intelligence (AI) methods for improving the performance of diagnosis is justified. Reported work presents a new and efficient artificial intelligence technique that is support vector machine (SVM) for transformer fault diagnosis using dissolved gas analysis. The proposed method i.e. Support Vector Machine is a classification tool based on statistical learning theory. Here 3 types of multiclass SVM method that is One - against-One, One-against-All and binary decision tree have been used for the fault diagnosis. Each SVM method has been trained and tested with many practical fault data of power transformers.

Transformer diagnostics under dissolved gas analysis using Support Vector Machine

Economic Load Dispatch is one of the most important tasks to be performed in the operation and planning of a power system that decides the generation schedule of generating units with an objective of minimizing the total fuel cost. Normally, the fuel cost of generators can be treated as a quadratic function of real power generation. In fact, valve point loading effect in thermal power plants introduces discontinuity. The classical optimization methods require continuous differentiable objective functions; therefore they at times provide global minima. The evolutionary computation methods can handle non-differential and non-convex objective functions and provide global or near global optimum solutions. Evolutionary techniques such as Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) saw wide applications in economic load dispatch. Similar to evolutionary computation, physical behaviour intelligence called gravitational search intelligence is recently developed and has not been applied in many fields. Use of gravitational search intelligence not only avoids coding and monotonous decoding as prevalent in transformations of GA but also results in less burden on parameter settings, population size, number of iterations and no memory requirement of solution as in PSO. In this paper, Gravitational Search algorithm (GSA) is applied to economic load dispatch problem with valve point loading and Kron's loss. Its performance is compared for accuracy and speed with contemporaries heuristic search techniques like PSO, DE, and GA and traditional method sequential quadratic programming (SQP) on 3, 6, 13 and 40-unit test systems. The simulation results reveal that GSA has a great potential in handling complex optimization problems and capable to discover quality solution quickly even for large scale systems.

A new intelligence solution for power system economic load dispatch

This paper presents a novel combinational approach to network reconfiguration and DG installation problems for power loss minimization in distribution network. A multi swarm cooperative population based PSO method is used keeping in view the complexity of maintaining the radial structure and operational constraints. Multi swarm cooperative particle swarm optimization (MC-PSO) algorithm is an extension of PSO algorithm which is based on the cooperative behavior of different organisms living in an eco-system for surviving. The optimal points for DG installation and DG sizing are carried out simultaneously. Different approaches for loss minimization are undertaken and compared with each other. The effectiveness of the approach is tested with IEEE 33-bus and 69-bus test systems with encouraging results.

Solving distribution feeder reconfiguration and concurrent dg installation problems for power loss minimization by multi swarm cooperative PSO algorithm

Owing to frequent black-outs world wide, voltage collapse has received much attention in the electric utilities industry. This paper presents an artificial neural network based method for on-line voltage collapse margin estimation. Homotopy Continuation based Newton-Raphson method is used to drive system operating point to knee of nose curve. The distance of operating point from critical point, measured in terms of system loading may be regarded as margin to voltage collapse. This paper utilizes Kohonen classifier to estimate margin so that computational efforts are reduced compared to conventional methods. Also there is ample of saving in training time compared to error back propagation for parameter estimation. Kohonen neural network classifier transforms input patterns into neurons on the 2-dimensional grid. Power system conditions are assigned to neurons on the grid based on self-organized feature mapping. Finally these neurons are allocated voltage collapse margins corresponding to their sys...

Sushil Chauhan

Papers

A survey of emerging biometric modalities

Transformer diagnostics under dissolved gas analysis using Support Vector Machine

A new intelligence solution for power system economic load dispatch

Solving distribution feeder reconfiguration and concurrent dg installation problems for power loss minimization by multi swarm cooperative PSO algorithm

Kohonen neural network classifier for voltage collapse margin estimation