Nand Kishor

Islanding and Power Quality Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and

In this paper, comparative study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system is presented. The hybrid system consists of DG resources like photovoltaic, fuel cell, and wind energy systems connected to grid. The negative sequence component of the voltage signal is used in islanding detection of these resources from the grid. Voltage signal extracted directly at the point of common coupling is considered for detection of PQ disturbances. Further, the effect of variation of grid impedances on islanding and PQ disturbances and effect of islanding on the coherency between the energy resources is also presented in this paper. The study for different scenarios of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation of ST contour and WT signal is also reported in order to validate the graphical results. The results demonstrate the advantages of S -transform over WT in detection of islanding and different disturbances under noise-free as well as noisy scenarios.

S

The interconnection of the renewable-resources-based distributed generation (DG) system to the existing power system could lead to power quality (PQ) problems, degradation in system reliability, and other associated issues. This paper presents the classification of PQ disturbances caused not only by change in load but also by environmental characteristics such as change in solar insolation and wind speed. Various forms of sag and swell occurrences caused by change in load, variation in wind speed, and solar insolation are considered in the study. Ten different statistical features extracted through S-transform are used in the classification step. The PQ disturbances in terms of statistical features are classified distinctly by use of modular probabilistic neural network (MPNN), support vector machines (SVMs), and least square support vector machines (LS-SVMs) techniques. The classification study is further supported by experimental signals obtained on a prototype setup of wind energy system and PV system. The accuracy and reliability of classification techniques is also assessed on signals corrupted with noise.

-Transform

In this paper, islanding detection in a hybrid distributed generation (DG) system is analyzed by the use of hyperbolic S-transform (HST), time-time transform, and mathematical morphology methods. The merits of these methods are thoroughly compared against commonly adopted wavelet transform (WT) and S-transform (ST) techniques, as a new contribution to earlier studies. The hybrid DG system consists of photovoltaic and wind energy systems connected to the grid within the IEEE 30-bus system. Negative sequence component of the voltage signal is extracted at the point of common coupling and passed through the above-mentioned techniques. The efficacy of the proposed methods is also compared by an energy-based technique with proper threshold selection to accurately detect the islanding phenomena. Further, to augment the accuracy of the result, the classification is done using support vector machine (SVM) to distinguish islanding from other power quality (PQ) disturbances. The results demonstrate effective performance and feasibility of the proposed techniques for islanding detection under both noise-free and noisy environments, and also in the presence of harmonics.

Classification of Power Quality Disturbances Due to Environmental Characteristics in Distributed Generation System

Summary form only given. Penetration of distributed generation (DG) systems in conventional power systems leads to power quality (PQ) disturbances. This paper provides an improved PQ disturbances classification, which are associated with load changes and environmental factors. Various forms of PQ disturbances, including sag, swell, notch and harmonics, are taken into account. Several features are obtained through HS-transform, out of which optimal features are selected using a genetic algorithm (GA). These optimal features are used for PQ disturbances classification by employing support vector machines (SVM) and decision tree (DT) classifiers. The study is supported on three different case studies, considering experimental set-up prototypes for wind energy and photovoltaic (PV) systems, as well as the modified Nordic 32-bus test system. The robustness and precision of DT and SWM is performed with noise and harmonics in the disturbance signals, thus providing comprehensive results.

Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System

Wireless Sensor Networks (WSN) are resource constrained systems that needs efficient utilization of all resources. Clustering is well known technique for achieving high scalability and efficient resource allocation in WSN. One of the fundamental issues in cluster based networks is to determine the optimal number of clusters with the objective of minimizing the energy consumption. Considering its importance, a Fuzzy c-Means (FCM) clustering approach is proposed to determine the optimal number of clusters in WSN. The study considers the deployment of 100 nodes in 100×100 m2 area for random uniform distribution. The optimal number of clusters determined by FCM has been compared with those obtained by analytical method. Study on lifetime of wireless sensor networks is also presented with optimal clusters in network.

Optimal Feature and Decision Tree-Based Classification of Power Quality Disturbances in Distributed Generation Systems

Small-Signal Analysis of Autonomous Hybrid Distributed Generation Systems in Presence of Ultracapacitor and Tie-Line Operation This paper presents small-signal analysis of isolated as well as interconnected autonomous hybrid distributed generation system for sudden variation in load demand, wind speed and solar radiation. The hybrid systems comprise of different renewable energy resources such as wind, photovoltaic (PV) fuel cell (FC) and diesel engine generator (DEG) along with the energy storage devices such as flywheel energy storage system (FESS) and battery energy storage system (BESS). Further ultracapacitors (UC) as an alternative energy storage element and interconnection of hybrid systems through tie-line is incorporated into the system for improved performance. A comparative assessment of deviation of frequency profile for different hybrid systems in the presence of different storage system combinations is carried out graphically as well as in terms of the performance index (PI), ie integral square error (ISE). Both qualitative and quantitative analysis reflects the improvements of the deviation in frequency profiles in the presence of the ultracapacitors (UC) as compared to other energy storage elements.

Optimal number of clusters in wireless sensor networks: An FCM approach

In this paper, a modeling technique based on fuzzy system is used to predict the heat transfer coefficient in pool boiling of distilled water. To achieve this, an experimental investigation has been carried out for saturated boiling of distilled water from plain copper heating tube surface at atmospheric and sub-atmospheric pressures. An empirical correlation has been established to predict the boiling heat transfer coefficient. Further, the experimental data has been compared with those determined from the zero-order adaptive fuzzy model with heat flux as input variable. The representation accuracies of the pool boiling heat transfer coefficient for fuzzy model is very high as indicated from the performance index. The prediction performance of zero-order adaptive fuzzy model has also been compared with MATLAB based ANFIS function. Further, the statistical attributes are determined and compared with the experimental data set of pool boiling of distilled water available in open literatures.

Small-Signal Analysis of Autonomous Hybrid Distributed Generation Systems in Presence of Ultracapacitor and Tie-Line Operation

In this paper, the hydro power plant model (with penstock-wall elasticity and compressible water column effect) is simulated at random load disturbance variation with output as turbine speed for random gate position as input. The multilayer perceptron neural network (i.e. NNARX) and fused neural network and fuzzy inference system (i.e. ANFIS) for identification of turbine speed as output variable are reported. Emphasis is put on obtaining a generalized model, using (i) NNARX model and (ii) ANFIS model with membership functions defined by subtractive clustering for plant model representation under different values of water time constant. The comparative performance study between the two approaches is also addressed. In the end of the paper, an application of adaptive noise cancellation based on ANFIS model to identify the turbine speed dynamics is also discussed.

Adaptive fuzzy model identification to predict the heat transfer coefficient in pool boiling of distilled water

This paper describes dynamic modeling and proportional-plus-integral (PI) controlled based frequency regulation of isolated autonomous hybrid system comprising of different renewable energy sources such as wind and photovoltaic (PV) with fuel cell (FC) and diesel engine generator (DEG) along with the energy storage elements such as flywheel energy storage system (FESS) and battery energy storage system (BESS). Large band wind speed based on Van der Hovens model and wind turbine dynamics is considered in this paper in order to analyze the frequency deviation. Ultracapacitor (UC) as an alternative energy storing element along with PI controllers has been considered in the hybrid systems in order to minimize the frequency deviation. A comparative assessment of frequency deviation for different hybrid systems in the presence of different storage system combinations reflect the improvements of the deviation in frequency profile in the presence of the ultracapacitors (UC) as compared to other storage elements.

Adaptive intelligent hydro turbine speed identification with water and random load disturbances

Wireless Sensor Networks (WSN) are resource constrained systems that needs efficient utilization of all resources. Clustering is well known technique for achieving high scalability and efficient resource allocation in WSN. One of the fundamental issues in cluster based networks is to determine the optimal number of clusters with the objective of minimizing the energy consumption. Considering its importance, a Fuzzy c-Means (FCM) clustering approach is proposed to determine the optimal number of clusters in WSN. The study considers the deployment of 100 nodes in 100×100 m2 area for random uniform distribution. The optimal number of clusters determined by FCM has been compared with those obtained by analytical method. Study on lifetime of wireless sensor networks is also presented with optimal clusters in network.