Novel Open-Circuit Photovoltaic Bypass Diode Fault Detection Algorithm

In this article, a novel photovoltaic (PV) bypass diode fault detection algorithm is presented. The algorithm consists of three main steps. First, the threshold voltage of the current–voltage (I–V) curve is obtained using different failure bypass diode scenarios. Second, the theoretical prediction for the faulty regions of bypass diodes is calculated using the analysis of voltage drop in the I–V curve as well as the voltage at maximum power point. Finally, the actual I–V curve under any environmental condition is measured and compared with theoretical predictions. The proposed algorithm has been experimentally evaluated using a PV string that comprises three series-connected PV modules, and subtotal of nine bypass diodes. Various experiments have been conducted under diverse bypass diodes failure conditions. The achieved detection accuracy is always greater than 99.39% and 99.74% under slow and fast solar irradiance transition, respectively

Reliability Enhancing Control Algorithms for Two-Stage Grid-Tied Inverters

In the photovoltaic (PV) generation system, two types of grid-tied inverter systems are usually deployed: the single-stage grid-tied inverter system and the two-stage grid-tied inverter system. In the single-stage grid-tied inverter system, the input of the inverter is directly connected to the PV arrays, while an additional dc-dc stage is inserted between the PV arrays and the dc-ac inverter in the two-stage design. The additional dc-dc stage could provide a stable dc-link voltage to the inverter, which also enables new design possibilities, including the multi-MPPT operation and solar-plus-storage application. Thus, the two-stage grid-tied inverter has been widely used in the PV generation system.As the core component of the PV generation system, the reliability of the grid-tied inverter determines the overall robustness of the system. The two-stage grid-tied inverter system includes three parts: the dc-dc stage, dc-link capacitor, and dc-ac inverter. Thus, the reliability of the two-stage grid-tied inverter relies on the reliability of each part. The dc-dc stage is used to provide a stable dc-link voltage to the inverter. However, when the inverter stage provides constant power to the grid, the load of the dc-dc stage becomes the constant power load (CPL), which will deteriorate the stability of the dc-dc stage. The dc-link capacitor is used to attenuate the voltage ripple on the dc-link and balance the transient power mismatch between the dc-dc stage and the dc-ac stage. However, during the operation of the inverter system, the degradation of the capacitor will reduce the converter reliability, and even result in system failure. The inverter stage is connected to the grid through the output filter, and the LCL type filter has been commonly used due to its superior performance. The resonance of the LCL filter must be properly damped to enhance the inverter stability. However, the grid-side impedance will lead to the resonant frequency drifting of the LCL filter, which will worsen the stability margin of the inverter. Thus, the control design of the two-stage grid-tied inverter system must consider those reliability challenges. In this work, three control algorithms are proposed to solve the reliability challenges. For the dc-dc stage, an uncertainty and disturbance estimator (UDE) based robust voltage control scheme is proposed. The proposed voltage control scheme can actively estimate and compensate for the disturbance of the dc-dc stage. Both the disturbance rejection performance and the stability margin of the dc-dc stage, especially under the CPL, could be enhanced. For the dc-link capacitor, a high-frequency (HF) signal injection based capacitance estimation scheme is proposed. The proposed estimation scheme can monitor the actual dc-link capacitance in real-time. For the inverter stage, an adaptive extremum seeking control (AESC) based LCL filter resonant frequency estimation scheme is proposed. The AESC-based estimation scheme can estimate the resonant frequency of the LCL filter online. All the proposed reliability enhancing control algorithms could enhance the reliability of the two-stage grid-tied inverter system. Detailed theoretical analysis, simulation studies, and comprehensive experimental studies have been performed to validate the effectiveness

Reliability Enhancing Control Algorithms for Two-Stage Grid-Tied Inverters

In the photovoltaic (PV) generation system, two types of grid-tied inverter systems are usually deployed: the single-stage grid-tied inverter system and the two-stage grid-tied inverter system. In the single-stage grid-tied inverter system, the input of the inverter is directly connected to the PV arrays, while an additional dc-dc stage is inserted between the PV arrays and the dc-ac inverter in the two-stage design. The additional dc-dc stage could provide a stable dc-link voltage to the inverter, which also enables new design possibilities, including the multi-MPPT operation and solar-plus-storage application. Thus, the two-stage grid-tied inverter has been widely used in the PV generation system.As the core component of the PV generation system, the reliability of the grid-tied inverter determines the overall robustness of the system. The two-stage grid-tied inverter system includes three parts: the dc-dc stage, dc-link capacitor, and dc-ac inverter. Thus, the reliability of the two-stage grid-tied inverter relies on the reliability of each part. The dc-dc stage is used to provide a stable dc-link voltage to the inverter. However, when the inverter stage provides constant power to the grid, the load of the dc-dc stage becomes the constant power load (CPL), which will deteriorate the stability of the dc-dc stage. The dc-link capacitor is used to attenuate the voltage ripple on the dc-link and balance the transient power mismatch between the dc-dc stage and the dc-ac stage. However, during the operation of the inverter system, the degradation of the capacitor will reduce the converter reliability, and even result in system failure. The inverter stage is connected to the grid through the output filter, and the LCL type filter has been commonly used due to its superior performance. The resonance of the LCL filter must be properly damped to enhance the inverter stability. However, the grid-side impedance will lead to the resonant frequency drifting of the LCL filter, which will worsen the stability margin of the inverter. Thus, the control design of the two-stage grid-tied inverter system must consider those reliability challenges. In this work, three control algorithms are proposed to solve the reliability challenges. For the dc-dc stage, an uncertainty and disturbance estimator (UDE) based robust voltage control scheme is proposed. The proposed voltage control scheme can actively estimate and compensate for the disturbance of the dc-dc stage. Both the disturbance rejection performance and the stability margin of the dc-dc stage, especially under the CPL, could be enhanced. For the dc-link capacitor, a high-frequency (HF) signal injection based capacitance estimation scheme is proposed. The proposed estimation scheme can monitor the actual dc-link capacitance in real-time. For the inverter stage, an adaptive extremum seeking control (AESC) based LCL filter resonant frequency estimation scheme is proposed. The AESC-based estimation scheme can estimate the resonant frequency of the LCL filter online. All the proposed reliability enhancing control algorithms could enhance the reliability of the two-stage grid-tied inverter system. Detailed theoretical analysis, simulation studies, and comprehensive experimental studies have been performed to validate the effectiveness

Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors

Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for rotating machinery (RM) have witnessed substantial advancements in recent decades, driven by the increasing demand for enhanced reliability, efficiency, and safety in industrial operations. CM of valuable and high-cost machinery is crucial for performance tracking, reducing maintenance costs, enhancing efficiency and reliability, and minimizing mechanical failures. While various FDD methods for RM have been developed, these predominantly focus on signal processing diagnostics techniques encompassing time, frequency, and time-frequency domains, intelligent diagnostics, image processing, data fusion, data mining, and expert systems. However, there is a noticeable knowledge gap regarding the specific review of image-based CM and FDD. The objective of this research is to address the aforementioned gap in the literature by conducting a comprehensive review of image-based intelligent techniques for CM and fault FDD specifically applied to induction motors (IMs). The focus of the study is to explore the utilization of image-based methods in the context of IMs, providing a thorough examination of the existing literature, methodologies, and applications. Furthermore, the integration of image-based techniques in CM and FDD holds promise for enhanced accuracy, as visual information can provide valuable insights into the physical condition and structural integrity of the IMs, thereby facilitating early FDD and proactive maintenance strategies. The review encompasses the three main faults associated with IMs, namely bearing faults, stator faults, and rotor faults. Furthermore, a thorough assessment is conducted to analyze the benefits and drawbacks associated with each approach, thereby enabling an evaluation of the efficacy of image-based intelligent techniques in the context of CM and FDD. Finally, the paper concludes by highlighting key issues and suggesting potential avenues for future research

Investigation of Some Self-Optimizing Control Problems for Net-Zero Energy Buildings

Green buildings are sustainable buildings designed to be environmentally responsible and resource efficient. The Net-Zero Energy Building (NZEB) concept is anchored on two pillars: reducing the energy consumption and enhancing the local energy generation. In other words, efficient operation of the existing building equipment and efficient power generation of building integrated renewable energy sources are two important factors of NZEB development. The heating, ventilation and air conditioning (HVAC) systems are an important class of building equipment that is responsible for large portion of building energy usage, while the building integrated photovoltaic (BIPV) system is well received as the key technology for local generation of clean power. Building system operation is a low-investment practice that aims low operation and maintenance cost. However, building HVAC and BIPV are systems subject to complicated intrinsic processes and highly variable environmental conditions and occupant behavior. Control, optimization and monitoring of such systems desire simple and effective approaches that require the least amount of model information and the use of smallest number but most robust sensor measurements. Self-optimizing control strategies promise a competitive platform for control, optimization and control integrated monitoring for building systems, and especially for the development of cost-effective NZEB. This dissertation study endorses this statement with three aspects of work relevant to building HVAC and BIPV, which could contribute several small steps towards the ramification of the self-optimizing control paradigm. This dissertation study applies self-optimizing control techniques to improve the energy efficiency of NZEB from two aspects. First, regarding the building HVAC efficiency, the dither based extremum seeking control (DESC) scheme is proposed for energy efficient operation of the chilled-water system typically used in the commercial building ventilation and air conditioning (VAC) systems. To evaluate the effectiveness of the proposed control strategy, Modelica based dynamic simulation model of chilled water chiller-tower plant is developed, which consists of a screw chiller and a mechanical-draft counter-flow wet cooling tower. The steady-state performance of the cooling tower model is validated with the experimental data in a classic paper and good agreement is observed. The DESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the whole dynamic simulation model with different environment conditions. The simulation results demonstrated the effectiveness of the proposed ESC strategy under abrupt changes of ambient conditions and load changes. The potential for energy savings of these cases are also evaluated. The back-calculation based anti-windup ESC is also simulated for handling the integral windup problem due to actuator saturation. Second, both maximum power point tracking (MPPT) and control integrated diagnostics are investigated for BIPV with two different extremum seeking control strategies, which both would contribute to the reduction of the cost of energy (COE). In particular, the Adaptive Extremum Seeking Control (AESC) is applied for PV MPPT, which is based on a PV model with known model structure but unknown nonlinear characteristics for the current-voltage relation. The nonlinear uncertainty is approximated by a radial basis function neural network (RBFNN). A Lyapunov based inverse optimal design technique is applied to achieve parameter estimation and gradient based extremum seeking. Simulation study is performed for scenarios of temperature change, irradiance change and combined change of temperature and irradiance. Successful results are observed for all cases. Furthermore, the AESC simulation is compared to the DESC simulation, and AESC demonstrates much faster transient responses under various scenarios of ambient changes. Many of the PV degradation mechanisms are reflected as the change of the internal resistance. A scheme of detecting the change of PV internal shunt resistance is proposed using the available signals in the DESC based MPPT with square-wave dither. The impact of the internal resistance on the transient characteristics of step responses is justified by using the small-signal transfer function analysis. Simulation study is performed for both the single-string and multi-string PV examples, and both cases have demonstrated successful results. Monotonic relationship between integral error indices and the shunt internal resistance is clearly observed. In particular, for the multi-string, the inter-channel coupling is weak, which indicates consistent monitoring for multi-string operation. The proposed scheme provides the online monitoring ability of the internal resistance condition without any additional sensor, which benefits further development of PV degradation detection techniques

Bidirectional Electric Vehicles Service Integration in Smart Power Grid with Renewable Energy Resources

As electric vehicles (EVs) become more popular, the utility companies are forced to increase power generations in the grid. However, these EVs are capable of providing power to the grid to deliver different grid ancillary services in a concept known as vehicle-to-grid (V2G) and grid-to-vehicle (G2V), in which the EV can serve as a load or source at the same time. These services can provide more benefits when they are integrated with Photovoltaic (PV) generation. The proper modeling, design and control for the power conversion systems that provide the optimum integration among the EVs, PV generations and grid are investigated in this thesis. The coupling between the PV generation and integration bus is accomplished through a unidirectional converter. Precise dynamic and small-signal models for the grid-connected PV power system are developed and utilized to predict the system’s performance during the different operating conditions. An advanced intelligent maximum power point tracker based on fuzzy logic control is developed and designed using a mix between the analytical model and genetic algorithm optimization. The EV is connected to the integration bus through a bidirectional inductive wireless power transfer system (BIWPTS), which allows the EV to be charged and discharged wirelessly during the long-term parking, transient stops and movement. Accurate analytical and physics-based models for the BIWPTS are developed and utilized to forecast its performance, and novel practical limitations for the active and reactive power-flow during G2V and V2G operations are stated. A comparative and assessment analysis for the different compensation topologies in the symmetrical BIWPTS was performed based on analytical, simulation and experimental data. Also, a magnetic design optimization for the double-D power pad based on finite-element analysis is achieved. The nonlinearities in the BIWPTS due to the magnetic material and the high-frequency components are investigated rely on a physics-based co-simulation platform. Also, a novel two-layer predictive power-flow controller that manages the bidirectional power-flow between the EV and grid is developed, implemented and tested. In addition, the feasibility of deploying the quasi-dynamic wireless power transfer technology on the road to charge the EV during the transient stops at the traffic signals is proven