Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

Data-Driven Modeling with Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge Converter

For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficient thermal and magnetic models, unpredictable ambient conditions, etc. These inaccurate data-driven models based on pure simulation cannot represent the practical performance in physical world, hindering their applications in power converter modeling. To alleviate model discrepancy and improve accuracy in practice, this paper proposes a novel data-driven modeling with experimental augmentation (D2EA), leveraging both simulation data and experimental data. In D2EA, simulation data aims to establish basic functional landscape, and experimental data focuses on matching actual performance in real world. The D2EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter. The proposed D2EA approach realizes 99.92% efficiency modeling accuracy, and its feasibility is comprehensively validated in 2-kW hardware experiments, where the peak efficiency of 98.45% is attained. Overall, D2EA is data-light and can achieve highly accurate and highly practical data-driven models in one shot, and it is scalable to other applications, effortlessly.Comment: 11 page

Robust active damping in LCL-filter based medium-voltage parallel grid-inverters for wind turbines

LCL-filter based grid-tie inverters require damping for current-loop stability. There are only software modifications in active damping, whereas resistors are added in passive damping. Although passive damping incurs in additional losses, it is widely used because of its simplicity. This article considers the active damping in medium-voltage parallel inverters for wind turbines. Due to cost reasons, only minimal software changes are allowed and no extra sensors can be used. The procedure must be robust against line-inductance variations in weak grids. Double-update mode is needed so the resonance frequency is under the Nyquist limit. The bandwidth reduction when using active damping is also required to be known beforehand. Moreover, the design procedure should be simple without requiring numerous trial-and-error iterations. In spite of the abundant literature, the options are limited under these circumstances. Filter-based solutions are appropriate and a new procedure for tuning the notch-filter is proposed. However, this procedure requires that the resistance of the inductors is known and a novel filter-based solution is proposed that uses lag-filters. The lag-filters displace the phase angle at the resonance frequency so that the Nyquist stability criterion is fulfilled. Simulations and experiments with a 100 kVA prototype validate the analysis

Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing

The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult. To satisfy the ever-increasing demands in computing power, neuromorphic computing has emerged as an attractive alternative. This dissertation focuses on developing learning algorithm, hardware architecture, circuit components, and design methodologies for low-power neuromorphic computing that can be employed in various energy-constrained applications. A top-down approach is adopted in this research. Starting from the algorithm-architecture co-design, a hardware-friendly learning algorithm is developed for spiking neural networks (SNNs). The possibility of estimating gradients from spike timings is explored. The learning algorithm is developed for the ease of hardware implementation, as well as the compatibility with many well-established learning techniques developed for classic artificial neural networks (ANNs). An SNN hardware equipped with the proposed on-chip learning algorithm is implemented in CMOS technology. In this design, two unique features of SNNs, the event-driven computation and the inferring with a progressive precision, are leveraged to reduce the energy consumption. In addition to low-power SNN hardware, accelerators for ANNs are also presented to accelerate the adaptive dynamic programing algorithm. An efficient and flexible single-instruction-multiple-data architecture is proposed to exploit the inherent data-level parallelism in the inference and learning of ANNs. In addition, the accelerator is augmented with a virtual update technique, which helps improve the throughput and energy efficiency remarkably. Lastly, two techniques in the architecture-circuit level are introduced to mitigate the degraded reliability of the memory system in a neuromorphic hardware owing to the aggressively-scaled supply voltage and integration density. The first method uses on-chip feedback to compensate for the process variation and the second technique improves the throughput and energy efficiency of a conventional error-correction method.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144149/1/zhengn_1.pd

Model-based powertrain design and control system development for the ideal all-wheel drive electric vehicle

The transfer case based all-wheel drive electric vehicle (TCAWDEV) and dual-axle AWDEV have been investigated to balance concerns about energy consumption, drivability and stability of vehicles. However, the mentioned powertrain architectures have the torque windup issue or the wheel skidding issue. The torque windup is an inherent issue of mechanical linked all-wheel drive systems. The hydraulic motor-based or the electric motor-based ideal all-wheel drive powertrain can provide feasible solutions to the mentioned issues. An ideal AWDEV (IAWDEV) powertrain architecture and its control schemes were proposed by this research; the architecture has four independent driving motors in powertrain. The IAWDEV gives more control freedoms to implement active torque controls and traction mode controls. In essence, this research came up with the distributed powertrain concept, and developed control schemes of the distributed powertrain to replace the transfer case and differential devices. The study investigated the dual-loop motor control, the hybrid sliding mode control (HSMC) and the neural network predictive control to reduce energy consumption and achieve better drivability and stability by optimizing the torque allocation of each dependent wheel. The mentioned control schemes were respectively developed for the anti-slip, differential and yaw stability functionalities of the IAWDEV powertrain. This study also investigated the sizing method that the battery capacity was estimated by using cruise performance at 3% road grade. In addition, the model-based verification was employed to evaluate the proposed powertrain design and control schemes. The verification shows that the design and controls can fulfill drivability requirements and minimize the existing issues, including torque windup and chattering of the slipping wheel. In addition, the verification shows that the IAWDEV can harvest around two times more energy while the vehicle is running on slippery roads than the TCAWDEV and the dual-axle AWDEV; the traction control can achieve better drivability and lower energy consumption than mentioned powertrains; the mode control can reduce 3% of battery charge depleting during the highway driving test. It also provides compelling evidences that the functionalities achieved by complicated and costly mechanical devices can be carried out by control schemes of the IAWDEV; the active torque controls can solve the inherent issues of mechanical linked powertrains; the sizing method is credible to estimate the operation envelop of powertrain components, even though there is some controllable over-sizing

Risk-Based Machine Learning Approaches for Probabilistic Transient Stability

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive expansion plans or conservative operating limits. With the increasing system uncertainties and widespread electricity market deregulation, there is a strong inevitability to incorporate probabilistic transient stability (PTS) analysis. Moreover, the time-domain simulation approach, for transient stability evaluation, involving differential-algebraic equations, can be very computationally intensive, especially for a large-scale system, and for online dynamic security assessment (DSA). The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this research proposes risk-based, machine learning (ML) approaches, for PTS enhancement by replacing circuit breakers, including the impact of wind generation. Artificial Neural Network (ANN) was used for predicting the benefit-cost ratio (BCR) to reduce the computation effort. Moreover, both ANN and support vector machine (SVM) were used and consequently, were compared, for PTS classification, for online DSA. The training of the ANN and SVM was accomplished using suitable system features as inputs, and PTS status indicator as the output. DIgSILENT PowerFactory and MATLAB was utilized for transient stability simulations (for obtaining training data for ML algorithms), and applying ML algorithms, respectively. Results obtained for the IEEE 14-bus test system demonstrated that the proposed ML methods offer a fast approach for PTS prediction with a fairly high accuracy, and thereby, signifying a strong possibility for ML application in probabilistic DSA. Advisor: Sohrab Asgarpoo

DETERMINING GRID SECURITY THROUGH DYNAMIC STABILITY ANALYSIS OF MAJOR CONTINGECIES AND INCREASED RENEWABLE PENETRATION

The challenge of ensuring grid security becomes more complex with the advancement of new technology and major events causing widespread damage in the system. Threats of cyber-attacks create permutations of possible contingencies that may have never been considered in typical operations and planning. Natural disasters have caused devastating effects, taking out entire power systems and leaving thousands of customers without service for extended periods. The integration of more renewables into the grid creates dynamic stability concerns due to the replacement of large, rotating machines. In these examples, security can be assessed by studying dynamic stability, while also considering the consequences of each contingency or modification in the system.Security has been analyzed in three separate projects using various systems. The first project is Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS). In this project, a machine learning tool was used to determine security criteria for frequency, transient, and small-signal stability of a power system integrated with renewables. Security assessment is a fundamental function for both short-term and long-term power system operation. The developed data-driven security assessment (DSA) criteria uses machine learning to determine when it is necessary to trigger dynamic simulation by linking traditional isolated dynamic simulation with long-term scheduling. In the second project, a model of Puerto Rico’s 2018 transmission system was created. Simulations of major contingencies were performed on the Puerto Rico system, including the trip of main transmission corridors along the path of destructive Hurricane Maria. In the future, higher renewable penetration in the Puerto Rico system is expected. Therefore, studies were run at high solar penetration levels to assess dynamic stability under these conditions. Lastly, a cybersecurity study of a large system was also performed. Several scenarios were analyzed to determine stability boundaries and effects of possible targeted attacks. The goal was to determine critical contingencies that would cause system collapse

Distribution and regulation of ion channels in neurons: Quantitative studies of global ion channel transport and homeostatic synaptic scaling

A healthy neuron must continually produce millions of proteins and distribute them to function-specific regions of the cell. Among these proteins are ion channels that modulate neuronal excitability, allowing neurons to fulfill their primary role of information transfer. Neurons are unique among cells in their morphology, with projections that extend hundreds to thousands of microns. Neuron size and asymmetry pose a challenge for autoregulation of properties that require cargo transport across the cell. Homeostasis of ion channel localization has strong implications for neural excitability. This thesis concerns the intracellular distribution of ion channels in the context of longitudinal transport and global neuron regulation. The principal contributions are experimental measurements, data analysis, and modeling in the study of longitudinal neurite transport. Empirical investigations focus on the distribution and trafficking kinetics of ion channel Kv4.2, including quantitative measurements of both passive diffusion and active microtubule-based transport in both axons and dendrites (Chapters 3 and 5). Mass action models reveal that measured transport profiles corroborate discrepancies in Kv4.2 localization both between neurite types and along the somatodendritic axis (Chapter 4). Exchange between mobile and immobile fractions, inferred from analysis of repeated photobleaching, shapes intracellular distribution of Kv4.2 (Chapter 5). Further, the ensuing theoretical study surveys global regulation of ion channels, specifically for synaptic scaling, which requires cell-wide modulation of AMPA receptors for normalization of neural excitability. A unified model of synaptic potentiation, transport, and feedback reveals limitations imposed on synaptic scaling by neuron morphology. A neuron balances the stability, accuracy, and efficiency of synaptic scaling (Chapter 6).National Institutes of Health Oxford-Cambridge Scholars Gates Cambridge University of North Carolina Medical Scientist Training Progra