Biswarup Das

Fuzzy-logic-based fault classification scheme for digital distance protection

In this paper, a fuzzy-logic-based algorithm to identify the type of faults for digital distance protection system has been developed. The proposed technique is able to accurately identify the phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault resistances, inception angle, and loading levels. The proposed method needs only three line-current measurements available at the relay location and can perform the fault classification task in about a half-cycle period. Thus, the proposed technique is well suited for implementation in a digital distance protection scheme.

Multiobjective, Multiconstraint Service Restoration of Electric Power Distribution System With Priority Customers

In this paper, a technique based on nondominated sorting genetic algorithm-II (NSGA-II) is presented for solving the service restoration problem in an electric power distribution system. Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multiobjective, multiconstraint optimization problem. In contrast to the conventional genetic-algorithm (GA)-based approach, this approach does not require weighting factors for the conversion of such a multiobjective optimization problem into an equivalent single objective function optimization problem. In this work, various practical distribution system operation issues, such as the presence of priority customers, presence of remotely controlled, as well as manually controlled switches, etc. have also been considered. Based on the simulation results on four different distribution systems, the performance of the NSGA-II-based scheme has been found to be significantly better than that of a conventional GA-based method. Besides, to reduce the software runtime of the NSGA-II algorithm, a faster version of NSGA-II has also been implemented.

Combined Wavelet-SVM Technique for Fault Zone Detection in a Series Compensated Transmission Line

This paper presents a combined wavelet-support vector machine (SVM) technique for fault zone identification in a series compensated transmission line. The proposed method uses the samples of three line currents for one cycle duration to accomplish this task. Initially, the features of the line currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT). Subsequently, the extracted features are applied as inputs to a SVM for determining the fault zone (whether the fault is before or after the series capacitor, as observed from the relay point). The feasibility of the proposed algorithm has been tested on a 300-km, 400-kV series compensated transmission line for all the ten types of faults through detailed digital simulation using PSCAD/EMTDC. Upon testing on more than 25000 fault cases with varying fault resistance, fault inception angle, prefault power transfer level, percentage compensation level, and source impedances, the performance of the developed method has been found to be quite promising.

Coordination Between OLTC and SVC for Voltage Regulation in Unbalanced Distribution System Distributed Generation

This paper presents a two-stage approach for solving the optimal voltage regulation problem in unbalanced radial distribution system in the presence of photovoltaic (PV) generation. The on-load tap changer (OLTC) and static VAr compensator (SVC) have been considered as the voltage control devices in this work. The formulated voltage control problem is a mixed-integer nonlinear programming problem which remains unsolved even after 8 h due to its computational burden. However, the proposed two-stage approach can solve this problem in less than 10 min. The feasibility of the proposed approach has been demonstrated on a modified IEEE 123-bus radial distribution system.

Fuzzy logic-based fault-type identification in unbalanced radial power distribution system

In this paper, a fuzzy logic-based algorithm to identify the type of faults in radial, unbalanced distribution system has been developed. The proposed technique is able to accurately identify the phase(s) involved in all ten types of shunt faults that may occur in an electric power distribution system under different fault types, fault resistance, fault inception angle, system topology and loading levels. The proposed method needs only three line current measurements available at the substation and can perform the fault classification task in about half-cycle period. All the test results show that the proposed fault identifier is well suited for identifying fault types in radial, unbalanced distribution system.

Radial distribution system power flow using interval arithmetic

This paper reports on the application of interval arithmetic technique for balanced radial distribution system power flow analysis. Interval arithmetic takes care of the uncertainty in the input parameters and provides strict bounds for the solution of the problem. In this paper, uncertainties only in the input load parameters are considered. The results are compared with the results obtained from repeated load-flow simulations.

An Advanced IPFC Model to Reuse Newton Power Flow Codes

Complexities of computer program codes for Newton-Raphson load flow (NRLF) analysis are usually enhanced during power flow modeling of an interline power flow controller (IPFC). This is due to the fact that the contributions of the series converters of the IPFC are needed to be accounted for while computing bus power injections and Jacobian matrix elements. Also, the IPFC real power injection term along with its associated Jacobian matrix call for new codes to be written. In this paper an advanced IPFC model is proposed to address this issue, wherein an existing power system installed with IPFC(s) is transformed into an augmented equivalent network without any IPFC. To obtain the solution of the original network containing IPFC(s), the augmented network can easily be solved by reusing the existing NRLF codes, as this network is now devoid of any IPFC. Consequently, the complexities of the computer program codes are reduced substantially. Various practical device limit constraints of the IPFC can also be taken into account by the proposed model.

An Indirect UPFC Model to Enhance Reusability of Newton Power-Flow Codes

Power-flow modeling of a unified power-flow controller (UPFC) increases the complexities of the computer program codes for a Newton-Raphson load-flow (NRLF) analysis. This is due to the fact that modifications of the existing codes are needed for computing power injections, and the elements of the Jacobian matrix to take into account the contributions of the series and shunt voltage sources of the UPFC. Additionally, new codes for computing the UPFC real-power injection terms as well as the associated Jacobian matrix need to be developed. To reduce this complexity of programming codes, in this paper, an indirect yet exact UPFC model is proposed. In the proposed model, an existing power system installed with UPFC is transformed into an augmented equivalent network without any UPFC. Due to the absence of any UPFC, the augmented network can easily be solved by reusing the existing NRLF computer codes to obtain the solution of the original network containing UPFC(s). As a result, substantial reduction in the complexities of the computer program codes takes place. Additionally, the proposed model can also account for various practical device limit constraints of the UPFC.

Artificial neural network-based optimal capacitor switching in a distribution system

Abstract One of the most important control decision functions in a modern distribution automation system is volt–var control. The objective of volt–var control is to supply controlled reactive power by switching optimally the switchable capacitors installed in the distribution system such that the voltage drop and real power loss is minimum. Traditionally, this problem of optimal capacitor switching has been solved through various optimization techniques. However, as the time taken by these traditional optimization methods are quite significant, these methods may not be much suitable for online application. To reduce the time required to solve the optimal capacitor switching problem, an artificial neural network (ANN)-based approach has been developed in this paper. It has been found that the ANN-based technique is at least a 100 times faster than the traditional optimization methods for a practical number of capacitors in the system. Moreover, as the number of capacitors in the system increases, the effectiveness of the ANN over the traditional approach (in terms of the solution time) increases.

Consideration of input parameter uncertainties in load flow solution of three-phase unbalanced radial distribution system

In this paper, a technique based on interval arithmetic is presented for considering the uncertainties of the input parameters in the power flow solution of three-phase unbalanced radial distribution systems. The uncertainties in both the load demand and the feeder parameters have been considered. The results obtained from an interval arithmetic-based power flow solution have been compared with those obtained from repeated load flow simulations