This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions.

Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree

This paper presents a detection scheme for DC side short-circuit faults of photovoltaic (PV) arrays that consist of multiple PV panels connected in a series/parallel configuration. Such faults are nearly undetectable under low irradiance conditions, particularly, when a maximum power point tracking algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. The proposed fault detection scheme is based on a pattern recognition approach that employs a multiresolution signal decomposition technique to extract the necessary features, based on which a fuzzy inference system determines if a fault has occurred. The presented case studies (both simulation and experimental) demonstrate the effective and reliable performance of the proposed method in detecting PV array faults.

Fault Detection for Photovoltaic Systems Based on Multi-Resolution Signal Decomposition and Fuzzy Inference Systems

Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems

This paper presents a novel algorithm to determine the location of dc line faults in an HVDC system with a mixed transmission media consisting of overhead lines and cables, using only the measurements taken at the rectifier and inverter ends of the composite transmission line. The algorithm relies on the traveling-wave principle, and requires the fault-generated surge arrival times at two ends of the dc line as inputs. With accurate surge arrival times obtained from time-synchronized measurements, the proposed algorithm can accurately predict the faulty segment as well as the exact fault location. Continuous wavelet transform coefficients of the input signal are used to determine the precise time of arrival of traveling waves at the dc line terminals. Two possible input signals-the dc voltage measured at the converter terminal and the current through the surge capacitors connected at the dc line end-are examined and both signals are found to be equally effective for detecting the traveling-wave arrival times. Performance of the proposed fault-location scheme is analyzed through detailed simulations carried out using the electromagnetic transient simulation software PSCAD. The impact of measurement noise on the fault-location accuracy is also studied in this paper.

Location of DC Line Faults in Conventional HVDC Systems With Segments of Cables and Overhead Lines Using Terminal Measurements

Harris hawk optimization-based MPPT control for PV systems under partial shading conditions

A photovoltaic system is highly susceptible to partial shading. Based on the functionality of a photovoltaic system that relies on solar irradiance to generate electrical power, it is tacitly assumed that the maximum power of a partially shaded photovoltaic system always decreases as the shading heaviness increases. However, the literature has reported that this might not be the case. The maximum power of a partially shaded photovoltaic system under a fixed configuration and partial shading pattern can be highly insusceptible to shading heaviness when a certain critical point is met. This paper presents an investigation of the impact of partial shading and the critical point that reduce the susceptibility of shading heaviness. Photovoltaic string formed by series-connected photovoltaic modules is used in this research. The investigation of the P-V characteristic curve under different numbers of shaded modules and shading heaviness suggests that the photovoltaic string becomes insusceptible to shading heaviness when the shaded modules irradiance reaches a certain critical point. The critical point can vary based on the number of the shaded modules. The formulated equation in this research contributes to determining the critical point for different photovoltaic string sizes and numbers of shaded modules in the photovoltaic string.

https://www.mdpi.com/1996-1073/11/7/1860/pdf

Impact of Partial Shading on the P-V Characteristics and the Maximum Power of a Photovoltaic String

This paper presents the DC-DC converter model, implementation and simulation using state space modeling approach. Three DC-DC converters including Buck, Boost and Buck-Boost converter are developed using state-space modeling approach and implemented in Simulink. Each converter is modeled based on its circuit topology and its state space matrix are derived in detail. The state space model simulation results are comparable with circuitry model with only up to 0.0015V of tolerance. The simulation computation time has improved up to 7.8 times faster as compared to circuitry model. The state space model embed DC-DC converter into a single block, thus allowing buck, boost or buck-boost to be easily implemented and integrate into other larger system.

DC-DC converter modeling and simulation using state space approach

Numerical model framework of power quality events

Impact of bypass diode forward voltage on maximum power of a photovoltaic system under partial shading conditions

A multiple magnitude-duration (MMD) function is proposed for voltage sag acceptability assessment. The MMD function is a statistical representation of voltage sag derived from its rms analysis. The MMD function maps a series of correlated sag magnitude and duration points onto the standard power acceptability curve, revealing the dynamic characteristic of voltage sag across the standard power acceptability curve. It allows nonrectangular voltage sag, such as voltage sag due to transformer energization, induction motor starting, multistage fault, self-extinguishing fault, and voltage sag influenced by induction load to be assessed. The practicality and advantage of the proposed MMD acceptability assessment is evaluated using practical data and IEEE1159.2 waveforms set on an ITIC and SEMI F47 standard power acceptability curve. The evaluation results show that the proposed MMD function yields higher resolution and improved acceptability assessment accuracy compared to the conventional single-point mapping assessment.

Rodney H.G. Tan

Papers

Impact of Partial Shading on the P-V Characteristics and the Maximum Power of a Photovoltaic String

DC-DC converter modeling and simulation using state space approach

Numerical model framework of power quality events

Impact of bypass diode forward voltage on maximum power of a photovoltaic system under partial shading conditions

Voltage Sag Acceptability Assessment Using Multiple Magnitude-Duration Function