Dusmanta Kumar Mohanta

Communication Feasibility Analysis for Smart Grid With Phasor Measurement Units

The conventional power system is going through a paradigm change towards smart grid. Phasor measurement units (PMUs) have emerged as the edifice for information technology related to wide-area monitoring systems (WAMSs) for power system monitoring, information communication, and control purposes. The PMUs require communication links for a complex interconnected power grid, and proper spatial coordination plays a vital role for wireless communication network. Geographical information system (GIS) provides a rich set of functions to analyze the power network incorporating geospatial features. This paper investigates the impact of topological attributes on commissioning of wireless communication network for PMUs incorporating GIS aided analysis and to optimize one of the PMU location as main control station. This will not only serve for monitoring automation purposes, but will also ensure control automation. Case studies related to the eastern grid of India corroborate the efficacy of GIS-aided wireless communication for linking PMUs.

Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques : A comparison of results

This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power.

Fuzzy Markov model for determination of fuzzy state probabilities of generating units including the effect of maintenance scheduling

This paper presents a fuzzy Markov model to efficiently incorporate the influences of maintenance scheduling as well as aging of generating units on failure-repair cycle for computation of state probabilities. The proposed model, different from conventional models that are based on a probabilistic approach, employs a fuzzy set concept together with a probabilistic Markov model. This model incorporates fuzzy mean time to failure (FMTTF) and fuzzy mean time to repair (FMTTR) instead of crisp (expected) values for capturing the generating unit uncertainties more effectively through expert evaluation. The fuzzy state probabilities are computed from FMTTF and FMTTR values using fuzzy arithmetic operations for evaluation of the reliability index, fuzzy loss of load probability (FLOLP). Case studies for the maintenance scheduling of a captive power plant catering to an aluminum smelter have been formulated to demonstrate that fuzzy state probabilities as well as FLOLP provide a better explanation than the respective crisp counterparts.

A Wavelet-neuro-fuzzy Combined Approach for Digital Relaying of Transmission Line Faults

Abstract The proposed algorithm for fault location, different from conventional algorithms that are based on deterministic computations on a well-defined model to be protected, employs wavelet transform together with fuzzy inference system (FIS) and the adaptive neuro-fuzzy inference system (ANFIS) to incorporate expert evaluation so as to extract important features from wavelet multi-resolution analysis (MRA) coefficients for obtaining coherent conclusions regarding fault location. Simulation results indicate that both the classification and localization algorithms are immune to the effects of fault inception angle, impedance and distance. The most significant contribution of this article is that the proposed ANFIS approach has superiority over FIS for location of transmission line faults and thus can be used as an effective tool for real-time digital relaying purposes.

Smart Fault Location for Smart Grid Operation Using RTUs and Computational Intelligence Techniques

The smart grid aims to improve the quality and reliability of power at the generation, transmission, and distribution levels. The transmission lines can be considered the arteries of the power system, as they carry power over long distances and are exposed to difficult terrain. Transmission line protection philosophy is undergoing a change of paradigm with the advent of digital relays and high-speed broadband communication with the global positioning system (GPS). This paper explores the possibility of transmission line protection for a multigenerator system using wavelet multiresolution analysis (MRA) and computational intelligence techniques in conjunction with GPS. The inputs for the wavelet transform are the synchronized currents measured from remote telemetry units (RTUs) using GPS technology on different buses. The smart location technique uses a wavelet MRA technique to extract the features of the transient current signals based on the harmonics generated at the instant of occurrence of faults due to the abrupt changes of currents in a three-phase transmission line. These extracted features, with such computational intelligence techniques as an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), lead the grid toward smarter strategies for locating the fault distance. A comparative study establishes the superiority of ANFIS over ANNs for more accurate and reliable smart fault location. Furthermore, the efficacy of the proposed method is validated through a Monte Carlo simulation to incorporate the stochastic (random) nature of fault occurrence in a real-time transmission line. The most significant contribution of this paper is that the proposed smart location technique is immune to the effects of the fault inception angle (FIA), fault impedance, fault distance, and power angle. The results contained in this paper validate the use of the proposed algorithm for the real-time smart grid operation of transmission lines.

Power quality analysis using Discrete Orthogonal S-transform (DOST)

Power quality is one of the major issues in the modern electrical power world. The widespread usage of power electronic devices and non-linear loads make the power system more vulnerable to the power quality disturbances. As the power grids are expanding more and more because of the renewable sources, it necessitates responsive detection and accurate classification of power quality disturbances for corrective measures. This paper presents a new approach for power quality analysis using an orthogonal time-frequency representation of S-transform called Discrete Orthogonal S-transform (DOST). These power quality disturbances are generated on EHV transmission line under different fault and load condition. Different power quality disturbances have been analyzed using the short time Fourier transform (STFT), discrete wavelet transform (DWT), S-transform (ST) and DOST. Different case studies validate the superiority of DOST over other transforms in a more efficient way to monitor the power quality disturbances.

Robust transmission line fault classification using wavelet multi-resolution analysis☆

Abstract With the advent of high speed communication technology, global positioning system (GPS) and artificial intelligence (AI) techniques, there has been a paradigm shift in the parlance of power grid operation and control. The power system is in a phase of transition towards smart grid with the aid of these techniques to combat against contingencies, leading to reduction of failures and blackouts. The transmission lines are considered to be the back bone of the grid and traverse over difficult terrains. With the advancements in digital relaying and wide-area protection along with GPS technology, philosophy of protection has also undergone a paradigm change to take care of such challenges. This paper explores the possibility of transmission line protection for multi-generator system using wavelet Multi-Resolution Analysis (MRA) technique along with GPS. The inputs for the wavelet transform are the synchronized currents measured by remote telemetry units (RTUs) in conjunction with GPS technology at different buses. The classification algorithm uses wavelet MRA technique to extract features of the transient current signals based on harmonics generated due to abrupt change of currents in a three-phase transmission line caused by different faults. The major contribution of this paper is that the classification algorithm is immune to the effects of fault inception angle, fault impedance, fault distance and power angle. The results validate the efficacy of the proposed algorithm for real time smart grid operation.

Insulator condition analysis for overhead distribution lines using combined wavelet support vector machine (SVM)

Condition analysis of overhead power distribution system insulators using combined support vector machine (SVM) and wavelet multi-resolution analysis (MRA) seems to be promising for distribution system monitoring (DSM) automation to cope with the increasing system complexity. Though system well-being analysis for engineering applications has been used mostly for electric power system reliability studies, the same principle has been extended for assessing the condition of insulators in a distribution system based on the extent of their damage. Video surveillance with fixed cameras provide the required images of power lines along with insulators at regular intervals and same is sent to a control room using remote terminal units (RTUs) for analysis. Not only the health of the insulators, but also the sagging of the lines, breakage of both insulators and lines can be captured with such cameras. This paper mainly focuses on application of wavelet-transform based feature extraction for digital image processing and SVM for subsequent condition analysis of insulators. The most significant contribution of the paper is to compute the condition indices for overhead power distribution line insulators to overcome difficulties related to vehicular applications in video surveillance. The results contained in this paper validate the efficacy of the proposed methodology for wide-scale applications in overhead power distribution system monitoring (DSM) automation.

A DOST based approach for the condition monitoring of 11 kV distribution line insulators

The diminishing trend of reliability owing to possible power system failures has become a serious concern for the power system as a whole. It has necessitated the development of advanced protection methodologies employing advanced information technology and substation monitoring system (SMS) constitutes an integral part of such methodologies. The proposed scheme for condition monitoring of insulators, which serves as an augmented feature of SMS, aims at alleviating overall system reliability as well as power quality because cracked insulators cause disruption of power, thereby incurring heavy loss to the power system utilities. The image processing based video surveillance (VS) is incorporated to dispense with the cumbersome and time consuming conventional manual on-site detection using discrete orthogonal S-transform (DOST) in conjunction with some intelligent classification algorithms to ascertain the condition of the insulators.

Reliability Analysis of Phasor Measurement Unit Using Hidden Markov Model

Recently, electrical power systems have emerged as the largest and most complex machine because of its inordinate growth due to technological innovations and because of geographical sprawl. This growth has necessitated a commensurate system for operation, monitoring, and control, which is known as the wide-area measurement system (WAMS). The edifice of WAMS is the phasor measurement unit (PMU). Its failure adversely affects WAMS as a whole; therefore, reliability analysis of the PMU is extremely essential. This paper uses hidden Markov model to perform the reliability analysis of PMU. Although many reliability evaluation methods have been used for computing steady-state probabilities, the proposed methodology has the unique ability to compute transient probability, leading to better system monitoring during transient states to ensure proper restorative initiatives. This paper validates the efficacy of the proposed methodology with relevant case studies for real-time application in a modern power system to ensure reliable monitoring using PMUs.