This study presents an algorithm for detection, classification, and location of transmission line faults. A fault index based on features extracted from current signals using the alienation coefficient and Wigner distribution function has been proposed for the detection and classification of faults. Double line and double line to ground faults have been classified from each other using ground fault index based on negative sequence current. Statistical relations are proposed for the estimation of fault location using peak values of the proposed fault index. The results of different case studies established the effectiveness of the algorithm. The algorithm is found to be effective for providing protection to transmission line against various faults. This is achieved using current signals recorded on one terminal of the line. This makes the protection scheme less complex, fast and more economic due to the elimination of the requirement of communication channel and global positioning system synchronisation. The proposed protection scheme is also validated on a real‐time network of transmission utility. The effectiveness of the algorithm is established by comparing performance with reported algorithms.

The rapid growth of grid integrated renewable energy (RE) sources resulted in development of the hybrid grids. Variable nature of RE generation resulted in problems related to the power quality (PQ), power system reliability, and adversely affects the protection relay operation. High penetration of RE to the utility grid is achieved using multi-tapped lines for integrating the wind and solar energy and also to supply loads. This created considerable challenges for power system protection. To overcome these challenges, an algorithm is introduced in this paper for providing protection to the hybrid grid with high RE penetration level. All types of fault were identified using a fault index (FI), which is based on both the voltage and current features. This FI is computed using element to element multiplication of current-based Wigner distribution index (WD-index) and voltage-based alienation index (ALN-index). Application of the algorithm is generalized by testing the algorithm for the recognition of faults during different scenarios such as fault at different locations on hybrid grid, different fault incident angles, fault impedances, sampling frequency, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, and presence of noise. The algorithm is successfully tested for discriminating the switching events from the faulty events. Faults were classified using the number of faulty phases recognized using FI. A ground fault index (GFI) computed using the zero sequence current-based WD-index is also introduced for differentiating double phase and double phase to ground faults. The algorithm is validated using IEEE-13 nodes test network modelled as hybrid grid by integrating wind and solar energy plants. Performance of algorithm is effectively established by comparing with the discrete wavelet transform (DWT) and Stockwell transform based protection schemes.

As renewable energy (RE) penetration has a continuously increasing trend, the protection of RE integrated power systems is a critical issue. Recently, power networks developed for grid integration of solar energy (SE) have been designed with the help of multi-tapped lines to integrate small- and medium-sized SE plants and simultaneously supplying power to the loads. These tapped lines create protection challenges. This paper introduces an algorithm for the recognition of faults in the grid to which a solar photovoltaic (PV) system is integrated. A fault index (FI) was introduced to identify faults. This FI was calculated by multiplying the Wigner distribution (WD) index and Alienation (ALN) index. The WD-index was based on the energy density of the current signal evaluated using Wigner distribution function. The ALN-index was evaluated using sample-based alienation coefficients of the current signal. The performance of the algorithm was validated for various scenarios with different fault types at various locations, different fault incident angles, fault impedances, sampling frequencies, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, different types of transformer windings and the presence of noise. Two phase faults with and without the involvement of ground were differentiated using the ground fault index based on the zero sequence current. This study was performed on the IEEE-13 nodes test network to which a solar PV plant with a capacity of 1 MW was integrated. The performance of the algorithm was also tested on the western part of utility grid in the Rajasthan State in India where solar PV energy integration is high. The performance of the algorithm was effectively established by comparing it with the discrete Wavelet transform (DWT), Wavelet packet transform (WPT) and Stockwell transform-based methods.

For the modern power systems with intensive renewable energy sources integration, the uncertainty analysis of its performance become necessary for desired decision making. An appropriate choice of modelling and handling methodology, for uncertain parameters associated with the renewable energy, remains one of major concerns for the power system engineers. This paper presents a stochastic optimization framework for some critical decisions making on reactive power dispatch in two consecutive stages by incorporating both generation outputs of wind farms along with load demand uncertainties. The proposed method is generic for optimal reactive power dispatch in any modern wind-integrated power system. A methodology based on scenario-based analysis is adopted for handling these uncertainties. Moreover, a hybrid fuzzy evolutionary algorithm (HFEA) is applied to obtain high-quality solutions for this complex two-stage stochastic optimization problem. Furthermore, the proposed HFEA-based stochastic optimization approach is tested on IEEE 14-bus power system with two distinct wind farms. The simulation results prove the robustness of HFEA for generating feasible and optimal solutions for all the considered scenarios.