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.

Particle swarm optimization (PSO) is envisioned as potential solution to overcome maximum power point tracking (MPPT) problems. Nevertheless, conventional PSO suffers from large transient oscillation, slow convergence and tedious parameter tuning when tracking global MPP (GMPP) under partial shading conditions (PSC), leading to poor efficiency and significant power loss. Therefore, a modified PSO hybridized with adaptive local search (MPSO-HALS) is designed as a robust, real-time MPPT algorithm. A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. Additionally, a rank-based selection scheme is adopted to choose best half of population for subsequent global and local search modes. A modified global search method with fewer parameters is devised to rapidly identify approximated location of GMPP. Finally, a modified local search method using Perturb and Observe with adaptive step size method (P&O-ASM) is proposed to refine the near-optimal duty cycle and track GMPP with negligible oscillations. MPSO-HALS is implemented into low-cost microcontroller for real-time application. Extensive studies prove the proposed algorithm outperforms bat algorithm (BA), improved grey wolf optimizer (IGWO), conventional PSO and P&O, with convergence time shorter than 0.3 s and tracking accuracy above 99% under different complex PSCs.

Adaptive protection schemes (APS) have gained prominence in maintaining the integrity of overcurrent relay (OCR) settings in reconfigurable networks. While many APSs rely on supervisory control and data acquisition systems, they are very expensive and expose the system to vulnerabilities arising from communication failures. Recent studies have proposed communication-less APSs to address this issue by relying on data-mining algorithms equipped with real-time fault voltage-current information. However, the OCR settings are computed and updated as the fault occurs, inevitably causing prolonged OCR tripping in these schemes. This contradicts with the APS’ original purpose of minimizing OCR operation time and consequent equipment damage. Thus, a load flow-based APS that addresses this flaw is proposed to achieve primary-backup OCR coordination in a highly reconfigurable system. Network topologies are first categorized into OCR setting groups via clustering analysis. A nonparametric probability model is developed to evaluate the probability of network topologies at a measured load flow. Then, a machine learning model deployed in a local controller selects the correct setting groups based on the calculated probabilities. The proposed APS achieves high accuracies and low OCR operating times in the IEEE 33-bus test distribution system under varying load conditions and network topologies.

Conventional perturb and observe (P&O) algorithm fails to track global maximum power point (GMPP) under complex partial shading conditions (PSC). While many of the latest maximum power point tracking (MPPT) algorithms are designed to handle simpler PSCs with fewer peaks, their capability to handle highly complex PSCs remains uncertain. This study presented more practical, challenging, and complex PSCs that have over five peaks and extremely close peak values. A new deterministic peak hopping (PH)-based MPPT algorithm with simple mechanisms is proposed to address these complex PSCs. An agent is utilized to scan and hop between the lower and higher duty cycle regions of P-V curve with optimum step size, thereby effectively narrowing down the tracking region, moving towards the GMPP. Additionally, the proposed algorithm utilizes an adjustable sampling time during scanning and hopping process to accelerate the convergence. Extensive simulation studies have revealed the effectiveness of the proposed algorithm in tracking GMPP. Moreover, the proposed algorithm outperforms five of the latest MPPT algorithms. In experimental setup, the proposed algorithm is successfully implemented into real-time TI C2000 microcontroller and performed robustly using ITECH IT6012C-800-40 PV simulator, achieving tracking time shorter than 0.83s and tracking accuracy over 98.70%.

Author(s):J C Teo, Rodney H G Tan, V H Mok, Vigna K Ramachandaramurthy, C K Tanc

A real time power quality event detection and logging system using continuous wavelet transform is presented in this paper. A virtual instrument concept is adopted to implement the continuous wavelet transform in real time. The measurement hardware consists of data acquisition module with USB interface and the wavelet event detection algorithm was developed using MATLAB with graphical user interface to display the wavelet coefficients in real time and the recorded waveform. The proposed algorithm is also capable to perform voltage sag and voltage transient classification for event count upon event detection. A test bench setup with voltage sag generator is used to test the performance of the proposed algorithm. With the right setting and calibration, the proposed algorithm shows superior performance with no miss detection on the test bench. This paper shows continuous wavelet transform performs in real time is made possible and it is a promising signal processing algorithm for power quality event detection application.