A new intelligent algorithm for online voltage stability assessment and monitoring

This paper presents a new approach for assessing power system voltage stability based on artificial feed forward neural network (FFNN). The approach uses real and reactive power, as well as voltage vectors for generators and load buses to train the neural net (NN). The input properties of the NN are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The performance of the trained NN is investigated on two systems under various voltage stability assessment conditions. Main advantage is that the proposed approach is fast, robust, accurate and can be used online for predicting the L-indices of all the power system buses simultaneously. The method can also be effectively used to determining local and global stability margin for further improvement measures.

Intelligent thermal energy meter cum controller for solar water heating systems

A microcontroller based, thermal energy meter cum controller (TEMC) suitable for solar thermal systems has been developed. It monitors solar radiation, ambient temperature, fluid flow rate, and temperature of fluid at various locations of the system and computes the energy transfer rate. It also controls the operation of the fluid-circulating pump depending on the temperature difference across the solar collector field. The accuracy of energy measurement is +/-1.5%. The instrument has been tested in a solar water heating system. Its operation became automatic with savings in electrical energy consumption of pump by 30% on cloudy days.

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Intelligent prediction of the stress-strain response of intact and jointed rocks

An application of Artificial Neural Networks for predicting the stress-strain response of jointed rocks under different confining pressures is presented in this paper. Rocks of different compressive strength with different joint properties (frequency, orientation and strength of joints) are considered in this study. The database for training the neural network is formed from the results of triaxial compression tests on different intact and jointed rocks with different joint properties tested at different confining pressures reported by various researchers in the literature. The network was trained using a three-layered network with the feed-forward back propagation algorithm.About 85% of the data was used for training and the remaining 15% was used for testing the network. Results from the analyses demonstrated that the neural network approach is effective in capturing the stress-strain behaviour of intact rocks and the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different jointed rocks, whose intact strength varies from 11.32 MPa to 123 MPa, spacing of joints varies from 10 cm to 100 cm. and confining pressures range from 0 to 13.8 MPa. (C) 2010 Elsevier Ltd. All rights reserved.

Intelligent Processing Of Zno-Based Ceramics

The Intelligent Decision Support System (IDSS), also called an expert system, is explained. It was then applied to choose the right composition and firing temperature of a ZnO based varistor. 17 refs.

Studies of cryotreatment on the performance of integral diaphragm pressure transducers for space application

The integral diaphragm pressure transducers machined out of precipitation hardened martensite stainless steel (APX4) are widely used for propellant pressure measurements in space applications. These transducers are expected to exhibit dimensional stability and linearity for their entire useful life. These vital factors are very critical for the reliable performance and dependability of the pressure transducers. However, these transducers invariably develop internal stresses during various stages of machining. These stresses have an adverse effect on the performance of the transducers causing deviation from linearity. In order to eliminate these possibilities, it was planned to cryotreat the machined transducers to improve both the long-term linearity and dimensional stability. To study these effects, an experimental cryotreatment unit was designed and developed based on the concept of indirect cooling using the concept of cold nitrogen gas forced closed loop convection currents. The system has the capability of cryotreating large number of samples for varied rates of cooling, soaking and warm-up. After obtaining the initial levels of residual stress and retained austenite using X-ray diffraction techniques, the pressure transducers were cryotreated at 98 K for 36 h. Immediately after cryotreatment, the transducers were tempered at 510 degrees C for 3 h in vacuum furnace. Results after cryo treatment clearly indicated significant reduction in residual stress levels and conversion of retained austenite to martensite. These changes have brought in improvements in long term zero drift and dimensional stability. The cryotreated pressure transducers have been incorporated for actual space applications. (c) 2010 Published by Elsevier Ltd.

Elasto-Electrostatic Analysis of Circular Microplates Used in Capacitive Micromachined Ultrasonic Transducers

The active structural component of a capacitive micromachined ultrasonic transducer (CMUT) is the top plate which vibrates under the influence of a time-varying electrostatic force thereby producing ultrasound waves of the desired frequency in the surrounding medium. Analysis of MEMS devices which rely on electrostatic actuation is complicated due to the fact that the structural deformations alter the electrostatic forces, which redistribute and modify the applied loads. Hence, it becomes imperative to consider the electrostatics-structure coupling aspect in the design of these devices. This paper presents an approximate analytical solution for the static deflection of a thin, clamped circular plate caused by electrostatic forces which are inherently nonlinear. Traditionally, finite element simulations using some commercial software such as ANSYS are employed to determine the structural deflections caused by electrostatic forces. Since the structural deformation alters the electrostatic field, a coupled-field simulation is required wherein the electrostatic mesh is continuously updated to coincide with the deflection of the structure. Such simulations are extremely time consuming, in addition to being nontransparent and somewhat hard to implement. We employ the classical thin-plate theory which is adequate when the ratio of the diameter to thickness of the plate is very large, a situation commonly prevalent in many MEMS devices, especially the CMUTs. We solve the thin-plate electrostatic-elastic equation using the Galerkin-weighted residual technique, under the assumption that the deflections are small in comparison to the thickness of the plate. The evaluation of the electrostatic force between the two plates is simplified due to the fact that the electrostatic gap is much smaller than the lateral dimensions of the device. The results obtained are compared to those found from ANSYS simulations and an excellent agreement is observed between the two. The pull-in voltage predicted by our model is close to the value predicted by ANSYS simulations.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

ICADA: Intelligent computer aided defect analysis for castings

An intelligent computer aided defect analysis (ICADA) system, based on artificial intelligence techniques, has been developed to identify design, process or material parameters which could be responsible for the occurrence of defective castings in a manufacturing campaign. The data on defective castings for a particular time frame, which is an input to the ICADA system, has been analysed. It was observed that a large proportion, i.e. 50-80% of all the defective castings produced in a foundry, have two, three or four types of defects occurring above a threshold proportion, say 10%. Also, a large number of defect types are either not found at all or found in a very small proportion, with a threshold value below 2%. An important feature of the ICADA system is the recognition of this pattern in the analysis. Thirty casting defect types and a large number of causes numbering between 50 and 70 for each, as identified in the AFS analysis of casting defects-the standard reference source for a casting process-constituted the foundation for building the knowledge base. Scientific rationale underlying the formation of a defect during the casting process was identified and 38 metacauses were coded. Process, material and design parameters which contribute to the metacauses were systematically examined and 112 were identified as rootcauses. The interconnections between defects, metacauses and rootcauses were represented as a three tier structured graph and the handling of uncertainty in the occurrence of events such as defects, metacauses and rootcauses was achieved by Bayesian analysis. The hill climbing search technique, associated with forward reasoning, was employed to recognize one or several root causes.

Fuzzy logic intelligent system for gas turbine module and system fault isolation

A fuzzy logic intelligent system is developed for gas-turbine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. These four measurements are also called the cockpit parameters and are typically found in almost all older and newer jet engines. The fuzzy logic system uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. It automates the reasoning process of an experienced powerplant engineer. Tests with simulated data show that the fuzzy system isolates faults with an accuracy of 89% with only the four cockpit measurements. However, if additional pressure and temperature probes between the compressors and before the burner, which are often found in newer jet engines, are considered, the fault isolation accuracy rises to as high as 98%. In addition, the additional sensors are useful in keeping the fault isolation system robust as quality of the measured data deteriorates.