449 research outputs found

    Neural Network Fault Recognition in Power Systems with High Penetrations of Inverter-Based Resources

    Get PDF
    The growing demand for renewable energy resources (RER) has led to increased integration of inverter-based resources (IBRs), into existing power distribution and transmission networks. However, RER locations are often not ideally suited for direct integration, necessitating a restructuring of the grid from a traditional radial network to a more complex mesh network topology. This transition presents challenges in terms of protection and coordination, as IBRs exhibit atypical responses to power system anomalies compared to conventional synchronous generation. To address these challenges and support existing power system protection infrastructure, this work explores the incorporation of machine learning algorithms. Specifically, an optimized convolutional neural network (CNN) is developed for real-time application in power system protection schemes. The focus is on prioritizing key performance metrics such as recall, specificity, speed, and the reduction of computational resources required for effective protection. The machine learning model is trained to differentiate between healthy system dynamics and hazardous conditions, such as faults, in the presence of IBRs. By analyzing data retrieved from an IEEE 34-bus 24kV distribution network, the model's application is demonstrated and its performance is evaluated. A photovoltaic (PV) source was incorporated into the IEEE 34-bus distribution feeder model at the end of the feeder. By adding a PV source at the end of the feeder, IBR characteristics, such as its response to system anomalies can be monitored through the model. Once the modified IEEE 34-bus distribution feeder model with the PV source was set up, various system anomalies were simulated to create a diverse dataset for training the machine learning (ML) model. These anomalies included; load rejection - a sudden and complete removal of load from the distribution network, simulating a scenario where a significant portion of the load disconnects from the grid, load addition - a sudden and significant increase in load demand, representing a scenario where new loads are connected to the grid, islanding - a scenario where the distribution feeder becomes electrically isolated from the main grid, with the PV source acting as a microgrid and supplying power to the local loads, and various types of faults, such as short-circuits or ground faults, occurring at different locations along the distribution line. To create a diverse dataset, model parameters were varied through 50 different iterations of each simulated anomaly scenario. These parameters included the PV system's capacity, the location of the anomaly on the feeder, the severity and duration of the anomaly, and other relevant grid parameters. For each iteration and anomaly scenario, the responses of the system were recorded, including voltage levels, current flows, and other relevant synchorphasors at the PV source's point of common coupling (PCC). These responses formed the dataset for training the ML model. The accumulated dataset was then used to train the various ML models, including the optimized convolutional neural network (CNN), to identify patterns and hidden characteristics in the data corresponding to different system anomalies. The training process involved feeding the model with input data from the various iterations and scenarios, along with corresponding labels indicating the type of anomaly present. By exposing the ML model to diverse scenarios and varying parameters, the model learns to generalize its understanding of system dynamics and accurately distinguish between healthy system states and hazardous conditions. The models in this work were specifically trained to recognize the various fault characteristics on the system. The trained model's ability to process time-series data and recognize anomalies from the accumulated dataset enhances power system protection infrastructure's capability to respond rapidly and accurately to various grid disturbances, ensuring the reliable and stable operation of the distribution network, especially in the presence of PV and other IBRs. The results show that the optimized CNN outperforms traditional machine learning models used in time-series data analysis. The model's speed and reliability make it an effective tool for identifying hidden characteristics in power system data without the need for extensive manual analysis or rigid programming of existing protection relays. This capability is particularly valuable as power grids integrate a higher penetration of IBRs, where traditional protection infrastructure may not fully account for their unique responses. The successful integration of the optimized CNN into power system protection infrastructure enhances the grid's ability to detect and respond to anomalies, such as faults, in a more efficient and accurate manner. By leveraging machine learning techniques, power system operators can better adapt to the challenges posed by the increasing presence of IBRs and ensure the continued stability and reliability of the distribution network

    Applications of Power Electronics:Volume 1

    Get PDF

    Power conversion for a modular lightweight direct-drive wind turbine generator

    Get PDF
    A power conversion system for a modular lightweight direct-drive wind turbine generator has been proposed, based on a modular cascaded multilevel voltage-source inverter. Each module of the inverter is connected to two generator coils, which eliminates the problem of DC-link voltage balancing found in multilevel inverters with a large number of levels.The slotless design of the generator, and modular inverter, means that a high output voltage can be achieved from the inverter, while using standard components in the modules. Analysis of the high voltage issues shows that isolating the modules to a high voltage is easily possible, but insulating the generator coils could result in a signicant increase in the airgap size, reducing the generator effciency. A boost rectier input to the modules was calculated to have the highest electrical effciency of all the rectier systems tested, as well as the highest annual power extraction, while having a competitive cost. A rectier control system, based on estimating the generator EMF from the coil current and drawing a sinusoidal current in phase with the EMF, was developed. The control system can mitigate the problem of airgap eccentricity, likely to be present in a lightweight generator. A laboratory test rig was developed, based on two 2.5kW generators, with 12 coils each. A single phase of the inverter, with 12 power modules, was implemented, with each module featuring it's own microcontroller. The system is able to produce a good quality AC voltage waveform, and is able to tolerate the fault of a single module during operation. A decentralised inverter control system was developed, based on all modules estimating the grid voltage position and synchronising their estimates. Distributed output current limiting was also implemented, and the system is capable of riding through grid faults

    Protection of Active Distribution Networks and Their Cyber Physical Infrastructure

    Get PDF
    Today’s Smart Grid constitutes several smaller interconnected microgrids. However, the integration of converter-interfaced distributed generation (DG) in microgrids has raised several issues such as the fact that fault currents in these systems in islanded mode are way less than those in grid connected microgrids. Therefore, microgrid protection schemes require a fast, reliable and robust communication system, with backup, to automatically adjust relay settings for the appropriate current levels according to the microgrid’s operation mode. However, risks of communication link failures, cyber security threats and the high cost involved to avoid them are major challenges for the implementation of an economic adaptive protection scheme. This dissertation proposes an adaptive protection scheme for AC microgrids which is capable of surviving communication failures. The contribution is the use of an energy storage system as the main contributor to fault currents in the microgrid’s islanded mode when the communication link fails to detect the shift to the islanded mode. The design of an autonomous control algorithm for the energy storage’s AC/DC converter capable of operating when the microgrid is in both grid-connected and islanded mode. Utilizing a single mode of operation for the converter will eliminate the reliance on communicated control command signals to shift the controller between different modes. Also, the ability of the overall system to keep stable voltage and frequency levels during extreme cases such as the occurrence of a fault during a peak pulse load period. The results of the proposed protection scheme showed that the energy storage -inverter system is able to contribute enough fault current for a sufficient duration to cause the system protection devices to clear the fault in the event of communication loss. The proposed method was investigated under different fault types and showed excellent results of the proposed protection scheme. In addition, it was demonstrated in a case study that, whenever possible, the temporary disconnection of the pulse load during the fault period will allow the utilization of smaller energy storage device capacity to feed fault currents and thus reduce the overall expenditures. Also, in this dissertation we proposed a hybrid hardware-software co-simulation platform capable of modeling the relation between the cyber and physical parts to provide a protection scheme for the microgrid. The microgrid was simulated on MATLAB/Simulink SimPowerSystems to model the physical system dynamics, whereas all control logic was implemented on embedded microcontrollers communicating over a real network. This work suggested a protection methodology utilizing contemporary communication technologies between multi-agents to protect the microgrid

    Power Quality in Electrified Transportation Systems

    Get PDF
    "Power Quality in Electrified Transportation Systems" has covered interesting horizontal topics over diversified transportation technologies, ranging from railways to electric vehicles and ships. Although the attention is chiefly focused on typical railway issues such as harmonics, resonances and reactive power flow compensation, the integration of electric vehicles plays a significant role. The book is completed by some additional significant contributions, focusing on the interpretation of Power Quality phenomena propagation in railways using the fundamentals of electromagnetic theory and on electric ships in the light of the latest standardization efforts

    Measurement, control and protection of microgrids at low frame rates supporting security of supply

    Get PDF
    Increasing penetrations of distributed generation at low power levels within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise unit cost, algorithms for the measurement of AC voltage and current waveforms should be implemented on a single microcontroller, which also carries out all other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although ADCs can be over-sampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality which may arise, even transiently, within a microgrid, battlefield, or disaster-relief scenario. This thesis analyses the potential magnitude of these interfering signals, and presents suitably tolerant architectures and algorithms for measurements of AC waveforms (amplitude, phase and frequency). These algorithms are shown to be robust and accurate, with harmonic content up to the level of 53% THD, and with the major algorithms executing at only 500 samples per second. This is achieved by the careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal to low-sample-rate Fourier analysis to sub-harmonic ripple removal. Algorithms for three-phase nodal power flow analysis are benchmarked on the Infineon TC1796 microcontroller and require less than 8% of the 2000μs frame time, leaving the remainder free for other algorithms. Furthermore, to optimise security of supply in a microgrid scenario, loss-of-mains must be detected quickly even when there is an accidental or deliberate balance between local active power generation and demand. The measurement techniques are extended to the detection of loss-of-mains using a new Phase Offset relay, in combination with a novel reactive power control technique to avoid the non-detection-zone. These techniques are tested using simulation, captured network transient events, and a real hardware microgrid including a synchronous generator and inverter

    Energy Management

    Get PDF
    Forecasts point to a huge increase in energy demand over the next 25 years, with a direct and immediate impact on the exhaustion of fossil fuels, the increase in pollution levels and the global warming that will have significant consequences for all sectors of society. Irrespective of the likelihood of these predictions or what researchers in different scientific disciplines may believe or publicly say about how critical the energy situation may be on a world level, it is without doubt one of the great debates that has stirred up public interest in modern times. We should probably already be thinking about the design of a worldwide strategic plan for energy management across the planet. It would include measures to raise awareness, educate the different actors involved, develop policies, provide resources, prioritise actions and establish contingency plans. This process is complex and depends on political, social, economic and technological factors that are hard to take into account simultaneously. Then, before such a plan is formulated, studies such as those described in this book can serve to illustrate what Information and Communication Technologies have to offer in this sphere and, with luck, to create a reference to encourage investigators in the pursuit of new and better solutions

    The Fifth NASA Symposium on VLSI Design

    Get PDF
    The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

    Applications of Power Electronics:Volume 2

    Get PDF
    corecore