Power Research

D. K. Chaturvedi ¹, Mayank Pratap Singh ¹, Md. Sharif Iqbal ¹, Vikas Pratap Singh ²

Affiliations
1 Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, U.P, IN
2 Central Power Research Institute, Bangalore,, IN

Abstract

The induction motor is commonly used in industries due to its rugged construction and almost no maintenance. To precisely control the induction motor, accurate estimation of parameters is required. Artificial Neural Network (ANN) is used in the past for parameter estimation. The conventional ANN has its own problems such as learning issues, unknown size of ANN and its connections, etc. To overcome some of its problems generalized neural network is used in this paper. The GNN is trained to estimate parameters of three phase induction motor. Experimental setup is developed in DEI, which is consisting of a 415 V, 3Φ squirrel cage induction motor, data acquisition system and on-line parameter estimator.

Keywords

Parameter estimation, three phase induction motor, ann , generalized neuron

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D. K. Chaturvedi ¹, Md. Sharif Iqbal ¹, Mayank Pratap Singh ¹, Vikas Pratap Singh ²

Affiliations
1 Dept. of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, U.P., IN
2 Central Power Research Institute, Bangalore, IN

Abstract

Induction motors are popularly used as an electric drives are the critical component in industrial systems. Most of the faults in induction motor are because of excessive heat generated in the machine. In the industrial application a large rating of induction motors are used which produce a significant heat. The heat is produced in the motor due to the different losses accumulated in the machine. Hence a healthy cooling system is always required to dissipate the heat and maintain the motor temperature within acceptable limits. This paper deals with the problem occurred specially in the cooling system under different operating conditions and investigate them. The on-line information is gathered from the machine about temperature, current and vibration signatures at different operating condition and the health of the cooling system is analyzed. Soft computing techniques are utilized for this purpose.

Keywords

Induction motor, cooling system, temperature, current signature, vibration, soft computing.

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D. K. Chaturvedi ¹, Mayank Pratap Singh ², Md. Sharif Iqbal ², Vikas Pratap Singh ³

Affiliations
1 Professor at Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, U.P., IN
2 Research Scholar at Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, U.P., IN
3 Senior Research Fellow at Central Power Research Institute, Bangalore, IN

Abstract

The parameters of induction motor depends on various factors such as: machine internal state, machine ageing, magnetic saturation, operating conditions, the coupling effect between the internal system and external system. The paper deals with an overview of parameter estimation of three phase induction motor using different soft computing techniques. The soft computing techniques which are considered in the paper are fuzzy system, artificial neural network (ANN), Neuro-Fuzzy, genetic algorithms (GA) and particle swarm optimization (PSO). It is observed that the estimated parameter using soft computing techniques were much closer to actual value.

Keywords

Induction motor, parameter estimation, soft computing techniques, ANN, Neuro-Fuzzy, GA, PSO

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D. K. Chaturvedi ¹, Rahul Umrao ², Vikas Pratap Singh ³

Affiliations
1 Professor, Department of Electrical Engineering, DEI Deemed University, Agra, IN
2 Allen Institute of Technology Kanpur, IN
3 Senior Research Fellow, ERED, CPRI, Bangalore, IN

Abstract

Fuzzy logic controller is based on human experience. Human experience is encoded in the form of fuzzy rule base to control the system. It is difficult to decide the size of fuzzy rule base. As the number of rules increases the performance of controller is better. At the same time its complexity increases which in turn affects the computation time and memory requirements. To overcome these problems, a Polar Fuzzy logic controller (PFC) is proposed for the load frequency control problem of nonlinear three area interconnected power system. In this paper, The PFC is made adaptive using Real Coded Genetic algorithm- fuzzy system (RCGAF) approach. The performance of simple PFC and adaptive PFC using RCGAF is compared with fuzzy and conventional PI controller.

Keywords

Load frequency control, adaptive polar fuzzy logic, real coded genetic algorithm, power systems.

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D. K. Chaturvedi ¹, Md. Sharif Iqbal ², Mayank Pratap Singh ², Vikas Pratap Singh ³

Affiliations
1 Professor, Department of Electrical Engineering, DEI Deemed University, Agra, IN
2 PhD Research Scholar, Department of Electrical Engineering, DEI Deemed University, Agra, IN
3 Senior Research Fellow, ERED, CPRI, Bangalore, IN

Abstract

Induction motor is singly excited and brushless,very simple, compact and extremely rugged in construction and most reliable and low cost motor. Although, these motors are reliable but often exposed to hostile environments during its operation which leads to early deterioration leading to the motors failure. Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and revenues. This paper deals with the study of identification of different type of faults and the health monitoring techniques commonly used in induction motors.

Keywords

Faults, health monitoring, fault diagnostic technique, induction motor.

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D. K. Chaturvedi ¹, Manmohan ², Rahul Umrao ³, Vikas Pratap Singh ⁴

Affiliations
1 Dept. of Electrical Engineering Faculty of Engineering, D.E.I. Dayalbagh, Agra, U.P.-282 005, IN
2 Department of Electrical Engineering Faculty of Engineering Dayalbagh Educational Institute (Deemed University) Dayalbagh, AGRA - 282005 (UP), IN
3 Dept. of Electrical Engineering Allen house Institute of Technology, Kulgaon Road, Rooma, Chakeri Ward, Kanpur, Uttar Pradesh - 208008, IN
4 IIT Jodhpur and Energy Efficiency and Renewable Energy Division, Central Power Research Institute, Bangalore - 560080, IN

Abstract

The Fuzzy Logic Controller has proven its worthiness for nonlinear complex systems. Multi-area power system is quite complex and nonlinear in nature. In this paper, Fuzzy logic controller (FLC) is developed for three area nonlinear power system. But there are inherent drawbacks of FLC such as its performance depends on number of rules, long computation time, large memory requirement etc. To overcome these problems, a polar fuzzy controller (PFC) is proposed to control the load frequency deviations in multi area power system. The PFC works on the basis that an angle acts as an input and controller response as an output. In conventional PI controller and FLC, two gains are to be tuned;whereas the PFC needs only one gain to be tuned, because the angle of PFC is calculated from the ratio of frequency deviation and the integral of frequency deviation. Hence, only one gain is sufficient to tune it. In PFC, only two rules are sufficient in the rule base. The work is extended to test the performance of proposed PFC in real time environment with the help of OPAL-RT simulator (OP 5142 v 10.2.4).

Keywords

Load frequency control, Polar fuzzy Controller, Real time simulation, AGC.

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Vikas Pratap Singh ¹, Vivek Vijay ¹, D. K. Chaturvedi ², Neha Adhikari ³

Affiliations
1 Indian Institute of Technology Jodhpur, IN
2 Department of Electrical Engineering Faculty of Engineering, D.E.I. Dayalbagh, Agra, U.P.-282 005, IN
3 ERED, Central Power Research Institute, Bangalore - 560080, IN

Abstract

The various forms of solar energy - solar heat, solar photovoltaic, solar thermal electricity, and solar fuels offer a clean, climate-friendly, very abundant and in-exhaustive energy resource to mankind. Solar power is the conversion of sun light into electricity, directly using photovoltaic (PV). The forecasting of energy Demands have become concerns for facility managers, and predicting energy generation plays a critical role in power-system management, scheduling, and dispatch operations. A reliable energy supply forecast helps to prevent unexpected loads and provides vital information for decisions made on energy generation and purchase. However, study of energy generation prediction by the photovoltaic (PV) system has been limited over the years, especially concerning short-term predictions. This study will helps in providing the details on different type of models and techniques of solar power forecasting.

Keywords

Solar forecasting; Forecasting models; Forecasting techniques.

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D. K. Chaturvedi ¹, A. P. Sinha ², Vikas Pratap Singh ³

Affiliations
1 Professor, Dept. of Electrical EngineeringFaculty of Engineering, D.E.I. Dayalbagh, Agra, U.P.-282 005, IN
2 Faculty, Dept. of Electrical Engineering, D.E.I. Dayalbagh, Agra, U.P.-282 005, IN
3 Energy Efficiency and Renewable Energy Division, Central Power Research Institute, Bangalore-560080, IN

Abstract

Soft computing techniques are extensively used for electrical load forecasting in the past such as ANN, Fuzzy Systems, GA etc.. ANN has some limitations, such unknown structure of ANN, Decision of neuron type, problem of training data and time, stuck in local minima etc. To overcome the drawbacks of ANN, a Generalized Neural Network (GNN) has been proposed. In this paper, different variants of GNN have been proposed to improve its performance such as GNN integrated with wavelet transform and trained with adaptive genetic algorithm and fuzzy system to forecast the short term week day electrical load. Performance of the proposed algorithm is compared with other GNN and its other variants on the basis of prediction error.

Keywords

Load Forecasting, ANN, Generalized neural network, Wavelet, Adaptive Genetic algorithms, Fuzzy systems.

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Vikas Pratap Singh ¹, Vivek Vijay ¹, S. H. Gaurishankar ¹, D. K. Chaturvedi ², N. Rajkumar ³

Affiliations
1 Indian Institute of Technology Jodhpur, Rajasthan, Old Residency Road, Ratanada, Jodhpur - 342 011, IN
2 Dayalbagh Educational Institute, Electrical Department Faculty of engineering, Dayalbagh Agra-282005, IN
3 ERED, Central Power Research Institute, Bangalore - 560 080, IN

Abstract

In 21st century solar power variability is an important issue due to grid integration. In these days grid integration is very popular because of heavy load. So solar power, wind power and conventional power are basic sources of grid integration. Solar power is playing a key role in grid integration. The main objective of this paper is to analyse solar power variability due to seasonal variation in Jodhpur. Jodhpur is known as sun-city for an average 320 sunny days in a year. Average solar insolation available in Jodhpur city is 5.7-6.0 kWh/m² per day. This is second highest insolation in the world. In this paper, the Solar power variability analysis is carried out based on the data collected from a typical 43 kW amorphous silicon solar photovoltaic system installed in Jodhpur. Mansoon, winter and summer seasons are used for analysis of variation in Photovoltaic Generation due to change of solar insolation. Output of solar photovoltaic system depends on solar insolation and in this paper we have analysed the variation in solar power according to rainy, winter and summer seasons and used artificial neural network to predict the power output from PV system. The paper showed that proposed ANN model is more accurate and study of variability in solar power can help in plant operation, power scheduling and dispatchability.

Keywords

No Keywords

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Vikas Pratap Singh ¹, Vivek Vijay ¹, S. H. Gaurishankar ¹, D. K. Chaturvedi ², N. Rajkumar ³

Affiliations
1 Indian Institute of Technology Jodhpur, Rajasthan, Old Residency Road, Ratanada, Jodhpur - 342 011, IN
2 Dayalbagh Educational Institute, Electrical Department Faculty of engineering, Dayalbagh Agra-282005, IN
3 ERED, Central Power Research Institute, Bangalore - 560 080, IN

Abstract

In 21st century solar power variability is an important issue due to grid integration. In these days grid integration is very popular because of heavy load. So solar power, wind power and conventional power are basic sources of grid integration. Solar power is playing a key role in grid integration. The main objective of this paper is to analyse solar power variability due to seasonal variation in Jodhpur. Jodhpur is known as sun-city for an average 320 sunny days in a year. Average solar insolation available in Jodhpur city is 5.7-6.0 kWh/m² per day. This is second highest insolation in the world. In this paper, the Solar power variability analysis is carried out based on the data collected from a typical 43 kW amorphous silicon solar photovoltaic system installed in Jodhpur. Mansoon, winter and summer seasons are used for analysis of variation in Photovoltaic Generation due to change of solar insolation. Output of solar photovoltaic system depends on solar insolation and in this paper we have analysed the variation in solar power according to rainy, winter and summer seasons and used artificial neural network to predict the power output from PV system. The paper showed that proposed ANN model is more accurate and study of variability in solar power can help in plant operation, power scheduling and dispatchability.

Keywords

No Keywords

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Vikas Pratap Singh ¹, Vivek Vijay ¹, B. Ravindra ¹, D. K. Chaturvedi ², Rajashekhar P. Mandi ³

Affiliations
1 Indian Institute of Technology Jodhpur, Old Residency Road, Jodhpur - 342 011, Rajathan, IN
2 Dayalbagh Educational Institute, Dayalbagh, Agra – 282005, Uttar Predesh, IN
3 Central Power Research Institute, Bangalore - 560 080, IN

Abstract

The main objective of this paper is to review the state of the art of IIT Jodhpur Rooftop installed 110 kW PV systems. This is done analyzing the operational data of 110 kW PV systems (43.30 kW located in Block 1 and 58.08 kW in Block 2). Performance analysis depends on three basic term of solar PV. How much energy do they produce? What level of performance is associated to their production? Which are the key parameters that most influence their quality? During the year 2011, the PV systems in Jodhpur, India have produced a mean annual energy of 1290.64 kWh/kWp in block-1 and 1290.64 kWh/kWp in Block-2. As a whole, the location of Solar PV system is the main reason of energy variability and system output. The overall mean Performance Ratio is 75% in both Blocks. Solar power variability depends on the two variables, deterministic and stochastic variable. In last few years researcher work on finding deterministic variable such as system losses (module efficiency, DC cable losses, inverter losses and AC cable losses) but these deterministic variables are not enough to give accurate forecasting so with the help of combined effect of these both variable give accurate plant performance and reliable solar power forecasting for solar power scheduling and dispatchability.

Keywords

PV System description, Roof of academic blocks, Performance analysis of solar PV, Performance ratio, Deterministic variables and Stochastic variables

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D. K. Chaturvedi ¹, M. P. Singh ²

Affiliations
1 Department of Electrical Engineering, Dayalbagh Educational Institute (Deemed University), Dayalbagh, Agra, IN
2 Faculty of Engineering and Technology, Dr. Ambedkar University, Agra, IN

Abstract

This paper deals with a system which monitors the health condition of a three phase induction motor by using infrared thermal images. Here two systems, real time and off line, are proposed to monitor the temperature variations and analyze the hot regions beyond the rated temperature in the three phase induction motor using infrared thermograms. This system helps to monitor the variation of temperature at the different parts of the induction motor. Abnormal temperature rise in any parts indicates the faults. This technique helps to prevent the parts of induction motor before any catastrophe would happen in the future. The color based segmentation technique is used to identify abnormal hot regions in the thermograms of three phase induction motor. A changing red color intensity algorithm is also implemented to recognize the hot spots and also the change in hotness in a particular area of induction motor to declare the health of that particular area. Similarly the conditions of various areas in the machine all together monitor the overall health of the Induction motor.

Keywords

Feature Selection, Fault Diagnostics, Health Monitoring, Intelligent System, Thermal Image.

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