System and Method for mining of Temporal Data

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series having events with start times and end times, a set of allowed dwelling times and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static VAr compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static Var compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Selection of Static VAR Compensator Location and Size for System Voltage Stability Improvement

This paper presents a methodology for selection of static VAR compensator location based on static voltage stability analysis of power systems. The analysis presented here uses the L-index of load buses, which includes voltage stability information of a normal load flow and is in the range of 0 (no load of system) to 1 (voltage collapse). An approach has been presented to select a suitable size and location of static VAR compensator in an EHV network for system voltage stability improvement. The proposed approach has been tested under simulated conditions on a few power systems and the results for a sample radial network and a 24-node equivalent EHV power network of a practical system are presented for illustration purposes. © 2000 Published by Elsevier Science S.A. All rights reserved.

Application of UPFC for system security improvement under normal and network contingencies

Electric power systems are exposed to various contingencies. Network contingencies often contribute to over-loading of network branches, unsatisfactory voltages and also leading to problems of stability/voltage collapse. To maintain security of the systems, it is desirable to estimate the effect of contingencies and plan suitable measures to improve system security/stability. This paper presents an approach for selection of unified power flow controller (UPFC) suitable locations considering normal and network contingencies after evaluating the degree of severity of the contingencies. The ranking is evaluated using composite criteria based fuzzy logic for eliminating masking effect. The fuzzy approach, in addition to real power loadings and bus voltage violations, voltage stability indices at the load buses also used as the post-contingent quantities to evaluate the network contingency ranking. The selection of UPFC suitable locations uses the criteria on the basis of improved system security/stability. The proposed approach for selection of UPFC suitable locations has been tested under simulated conditions on a few power systems and the results for a 24-node real-life equivalent EHV power network and 39-node New England (modified) test system are presented for illustration purposes.

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Optimal placement of Distributed Generation for a projected load increase using Relative Electrical Distance approach

There is a lot of pressure on all the developed and second world countries to produce low emission power and distributed generation (DG) is found to be one of the most viable ways to achieve this. DG generally makes use of renewable energy sources like wind, micro turbines, photovoltaic, etc., which produce power with minimum green house gas emissions. While installing a DG it is important to define its size and optimal location enabling minimum network expansion and line losses. In this paper, a methodology to locate the optimal site for a DG installation, with the objective to minimize the net transmission losses, is presented. The methodology is based on the concept of relative electrical distance (RED) between the DG and the load points. This approach will help to identify the new DG location(s), without the necessity to conduct repeated power flows. To validate this methodology case studies are carried out on a 20 node, 66kV system, a part of Karnataka Transco and results are presented.

Surface electrode switching of a 16-electrode wireless EIT system using RF-based digital data transmission scheme with 8 channel encoder/decoder ICs

A Radio Frequency (RF) based digital data transmission scheme with 8 channel encoder/decoder ICs is proposed for surface electrode switching of a 16-electrode wireless Electrical Impedance Tomography (EIT) system. A RF based wireless digital data transmission module (WDDTM) is developed and the electrode switching of a EIT system is studied by analyzing the boundary data collected and the resistivity images of practical phantoms. An analog multiplexers based electrode switching module (ESM) is developed with analog multiplexers and switched with parallel digital data transmitted by a wireless transmitter/receiver (T-x/R-x) module working with radio frequency technology. Parallel digital bits are generated using NI USB 6251 card working in LabVIEW platform and sent to transmission module to transmit the digital data to the receiver end. The transmitter/receiver module developed is properly interfaced with the personal computer (PC) and practical phantoms through the ESM and USB based DAQ system respectively. It is observed that the digital bits required for multiplexer operation are sequentially generated by the digital output (D/O) ports of the DAQ card. Parallel to serial and serial to parallel conversion of digital data are suitably done by encoder and decoder ICs. Wireless digital data transmission module successfully transmitted and received the parallel data required for switching the current and voltage electrodes wirelessly. 1 mA, 50 kHz sinusoidal constant current is injected at the phantom boundary using common ground current injection protocol and the boundary potentials developed at the voltage electrodes are measured. Resistivity images of the practical phantoms are reconstructed from boundary data using EIDORS. Boundary data and the resistivity images reconstructed from the surface potentials are studied to assess the wireless digital data transmission system. Boundary data profiles of the practical phantom with different configurations show that the multiplexers are operating in the required sequence for common ground current injection protocol. The voltage peaks obtained at the proper positions in the boundary data profiles proved the sequential operation of multiplexers and successful wireless transmission of digital bits. Reconstructed images and their image parameters proved that the boundary data are successfully acquired by the DAQ system which in turn again indicates a sequential and proper operation of multiplexers as well as the successful wireless transmission of digital bits. Hence the developed RF based wireless digital data transmission module (WDDTM) is found suitable for transmitting digital bits required for electrode switching in wireless EIT data acquisition system. (C) 2011 Elsevier Ltd. All rights reserved.

Electrical Resistance and Seebeck Coefficient in PbTe Nanowires

We address a physics-based simplified analytical formulation of the diffusive electrical resistance ( (Omega)) and Seebeck coefficient () in a PbTe nanowire dominated by acoustic phonon scattering under the presence of a low static longitudinal electric field. The use of a second-order nonparabolic electron energy band structure involving a geometry-dependent band gap has been selected in principle to demonstrate that the electron mean free path (MFP) in such a system can reach as low as about 8 nm at room temperature for a 10-nm-wide PbTe nanowire. This is followed by the formulation of the carrier back-scattering coefficient for determination of (Omega) and as functions of wire dimensions, temperature, and the field, respectively. The present analytical formulation agrees well with the available experimental data and may find extensive use in determination of various electrothermal transport phenomena in PbTe-based one-dimensional electron devices.

An Accurate Fractional Period Delay Generation System

An all-digital technique is proposed for generating an accurate delay irrespective of the inaccuracies of a controllable delay line. A subsampling technique-based delay measurement unit (DMU) capable of measuring delays accurately for the full period range is used as the feedback element to build accurate fractional period delays based on input digital control bits. The proposed delay generation system periodically measures and corrects the error and maintains it at the minimum value without requiring any special calibration phase. Up to 40x improvement in accuracy is demonstrated for a commercial programmable delay generator chip. The time-precision trade-off feature of the DMU is utilized to reduce the locking time. Loop dynamics are adjusted to stabilize the delay after the minimum error is achieved, thus avoiding additional jitter. Measurement results from a high-end oscilloscope also validate the effectiveness of the proposed system in improving accuracy.