A Hybrid Fault Recognition Algorithm Using Stockwell Transform and Wigner Distribution Function for Power System Network with Solar Energy Penetration

Performance Evaluation of a MW-Size Grid-Connected Solar Photovoltaic Plant Considering the Impact of Tilt Angle

This paper presents a study for the estimation of generation from a large-scale, grid-interfaced solar PV plant using the PVsyst software. This study aims to investigate the effect of tilt angle on the performance of the grid-integrated solar PV plant. Two types of tilt angle test plants, i.e., a fixed tilt angle of 30° (1 MW) and two seasonal tilt angles, in summer 13° and in winter 30° (2.5 MW), have been selected at the same location in Bikaner, India. The performance of the proposed test solar power plants, rated at 1 MW (fixed tilt angle) and 2.5 MW (two seasonal tilt angles), is established by comparing the results obtained using the PVsyst software with the practical data of annual solar insolation. It is established that the radiation incident on PV modules will increase by 2.41% if two seasonal tilt angles are considered. Hence, the annual capacity utilization factor (CUF) has increased by 0.26%. Furthermore, it is established that the proposed method’s performance is superior compared to the statistical methods reported in the literature.

Islanding detection in utility grid with renewable energy using rate of change of frequency and signal processing technique

<abstract> <p>This manuscript has introduced an algorithm based on current signals and frequency rate change (ROCOF) to identify islanding events. Current is analyzed by the use of Stockwell transform (ST) at 3.84 kHz sampling frequency (SF) and a median of absolute values of every column of output matrix (CSIRI) is computed. Rate of change of CSIRI (ROCOCSIRI) is computed. Proposed current based islanding recognition index (IRIC) is computed by multiplying ROCOF with CSIRI &amp; ROCOCSIRI and a weight factor (WC). Threshold values THI1 &amp; THI2 are selected 100 and 3000 for IRIC for identifying the Islanding condition. These are also effective to differentiate islanding conditions from non-islanding events which include both the faulty and operational events. Magnitude of IRIC is greater than 3000 for the faulty events and lower than 100 for operational events. For islanding events magnitude of IRIC falls in between the 100 and 3000. Algorithm is effective to identify and classify the events in three categories which are islanding events, faulty events and operational events effectively. Study is realized in MATLAB/Simulink scenario.</p> </abstract>

A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detecting ten kinds of faults. Using the proposed method, the maximum fault detection time is 13.8 ms and the maximum fault type identification time is 14.8 ms. No misjudgement happens under non-fault disturbance conditions. The simulations indicate that the proposed method can achieve fault detection and classification rapidly, accurately, and reliably.

Voltage-Based Hybrid Algorithm Using Parameter Variations and Stockwell Transform for Islanding Detection in Utility Grids

This paper has introduced an algorithm for the identification of islanding events in the remotely located distribution grid with renewable energy (RE) sources using the voltage signals. Voltage signal is processed using Stockwell transform (ST) to compute the median-based islanding recognition factor (MIRF). The rate of change in the root mean square (RMS) voltage is computed by differentiating the RMS voltage with respect to time to compute the voltage rate of change in islanding recognition factor (VRCIRF). The proposed voltage-based islanding recognition factor (IRFV) is computed by multiplying the MIRF and VRCIRF element to element. The islanding event is discriminated from the faulty and operational events using the simple decision rules using the peak magnitude of IRFV by comparing peak magnitude of IRFV with pre-set threshold values. The proposed islanding detection method (IDM) effectively identified the islanding events in the presence of solar energy, wind energy and simultaneous presence of both wind and solar energy at a fast rate in a time period of less than 0.05 cycles compared to the voltage change rate (ROCOV) and frequency change rate (ROCOF) IDM that detects the islanding event in a time period of 0.25 to 0.5 cycles. This IDM provides a minimum non-detection zone (NDZ). This IDM efficiently discriminated the islanding events from the faulty and switching events. The proposed study is performed on an IEEE-13 bus test system interfaced with renewable energy (RE) generators in a MATLAB/Simulink environment. The performance of the proposed IDM is better compared to methods based on the use of ROCOV, ROCOF and discrete wavelet transform (DWT).

Rooftop Solar PV Penetration Impacts on Distribution Network and Further Growth Factors—A Comprehensive Review

In order to meet the electricity needs of domestic or commercial buildings, solar energy is more attractive than other renewable energy sources in terms of its simplicity of installation, less dependence on the field and its economy. It is possible to extract solar energy from photovoltaic (PV) including rooftop, ground-mounted, and building integrated PV systems. Interest in rooftop PV system applications has increased in recent years due to simple installation and not occupying an external area. However, the negative effects of increased PV penetration on the distribution system are troublesome. The power loss, reverse power flow (RPF), voltage fluctuations, voltage unbalance, are causing voltage quality problems in the power network. On the other hand, variations in system frequency, power factor, and harmonics are affecting the power quality. The excessive PV penetration also the root cause of voltage stability and has an adverse effect on protection system. The aim of this article is to extensively examines the impacts of rooftop PV on distribution network and evaluate possible solution methods in terms of the voltage quality, power quality, system protection and system stability. Moreover, it is to present a comparison of the advantages/disadvantages of the solution methods discussed, and an examination of the solution methods in which artificial intelligence, deep learning and machine learning based optimization and techniques are discussed with common methods.