Zhehan Yi

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

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.

A Unified Control and Power Management Scheme for PV-Battery-Based Hybrid Microgrids for Both Grid-Connected and Islanded Modes

Battery storage is usually employed in photovoltaic (PV) system to mitigate the power fluctuations due to the characteristics of PV panels and solar irradiance. Control schemes for PV-battery systems must be able to stabilize the bus voltages as well as to control the power flows flexibly. This paper proposes a comprehensive control and power management system (CAPMS) for PV-battery-based hybrid microgrids with both ac and dc buses, for both grid-connected and islanded modes. The proposed CAPMS is successful in regulating the dc and ac bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads. Both simulation and experimental case studies are carried out to verify the performance of the proposed method.

Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine

Fault detection in photovoltaic (PV) arrays becomes difficult as the number of PV panels increases. Particularly, under low irradiance conditions with an active maximum power point tracking algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards. This paper proposes a fault detection algorithm based on multiresolution signal decomposition for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making. This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM. Both simulation and experimental case studies verify the accuracy of the proposed method.

Centralized finite control set model predictive control for multiple distributed generator small-scale microgrids

This paper proposes a control and power sharing strategy for small-scale microgrids in both grid-connected and islanded modes based on the centralized Finite Control Set Model Predictive Control (FCS-MPC). In grid-connected mode, the controller is capable of managing the output power of each Distributed Generator (DG) and enables flexible power regulation between the microgrid and the utility grid. In islanded mode, the controller regulates the microgrid voltage and frequency, and provides a precise power sharing scheme among DGs. In addition, the power sharing can be adjusted flexibly by changing the sharing ratio. The proposed control also enables plug-and-play operation. Moreover, a smooth transition between the two modes of operation is achieved without any disturbance in the system. Case studies are carried out to verify the proposed control strategy with the PSCAD/EMTDA software package.

A novel detection algorithm for Line-to-Line faults in Photovoltaic (PV) arrays based on support vector machine (SVM)

Line-to-Line (L-L) faults in Photovoltaic (PV) arrays prevent the PV system from producing maximum power, and if not cleared, may result in serious energy and revenue losses and cause fire hazards. Maximum Power Point Tracking (MPPT), a technique employed to maximize the power output of a PV array at different irradiance level, may potentially mask certain faults and make them undetectable by protection devices, especially when these faults occur under low solar irradiance condition or with high fault impedance. This paper proposes an innovative algorithm for detecting L-L faults in PV arrays based on support vector machine (SVM).