Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Introduction Part I: Preliminaries Linear Algebra and Preliminaries Disctrete-time Linear Systems Stochastic Processes Kalman Filter Part II: Realization Theory Realization of Deterministic Problems Stochastic Realization Theory I Stochastic Realization Theory II Part III: Subspace Identification Subspace Identification I: ORT Subspace Identification II: CCA Identification of Closed-loop System Appendices Least-squares Method Input Signals for System Identification Overlapping Parametrization Matlab(R) Programs Solutions to Problems

Subspace methods for system identification

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate Power-Voltage (P-V) curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximants method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P-V curve with moderate computational burden. The proposed method is illustrated in detail on a 4-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council (NPCC) 48-geneartor, 140-bus power system.

Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method

Overviews on the applications of the Kuramoto model in modern power system analysis

The research object of this paper is single-phase PWM rectifier, the purpose is to reduce the total harmonic distortion (THD) of the grid-side current. A model predictive current control (MPCC) with fixed switching frequency and dead-time compensation is proposed. First, a combination of an effective vector and two zero vectors is used to fix the switching frequency, and a current prediction equation based on the effective vector’s optimal action time is derived. The optimal action time is resolved from the cost function. Furthermore, in order to perfect the established prediction model and suppress the current waveform distortion as a consequence of the dead-time effect, the dead-time’s influence on the switching vector’s action time is analyzed, and the current prediction equation is revised. According to the experimental results, the conclusion is that, firstly, compared with finite-control-set model predictive control, proportional-integral-based instantaneous current control (PI-ICC) scheme and model predictive direct power control (MP-DPC), the proposed MPCC has the lowest current THD. In addition, the proposed MPCC has a shorter execution time than MP-DPC and has fewer adjusted parameters than PI-ICC. In addition, the dead-time compensation scheme successfully suppresses the zero-current clamping effects, and reduce the current THD.

https://www.mdpi.com/2079-9292/10/4/426/pdf

Model Predictive Current Control with Fixed Switching Frequency and Dead-Time Compensation for Single-Phase PWM Rectifier

Gaussian process surrogate model for an effective life assessment of transformer considering model and measurement uncertainties

Conventional Finite Control Set Model Predictive Control (FCS-MPC) for power electronics has many open issues like weighting factor calculation, computation efficiency with increased horizon and variable switching frequency. A novel predictive control strategy for direct current control of two level voltage source inverter with fixed switching frequency (FSF) is proposed. The FCS-MPC-FSF controller fixes the switching frequency of output variable by applying hard constraints on number of commutations. The controller guarantees fixed number of commutations in the half of switching period in order to obtain constant switching frequency. The algorithm is proposed for FCS-MPC with prediction horizon N=1. Simulation results shows the FCS-MPC-FSF control strategy is feasible and has good transient and steady state performance as compared to FCS-MPC strategy.

Finite control set model predictive control for two level inverter with fixed switching frequency

Synchronous generators continue to oscillate in multi-machine power systems in many coherent communities, and each community corresponds to the virtual generator. However, coherent communities can vary in response to different activities under different operating conditions. Hence it is necessary to conduct an online analysis however the resulting control actions are based on the system dynamical model. The system models formed using the first principle fail to represent the processes entirely throughout operating circumstances. Therefore a data-driven approach to system modeling is an effective resource for interpreting real-world activities and analyzing its effects in the future. For that, this paper introduces a novel approach for data-driven modeling based on dynamic mode decomposition (DMD) for the coherency identification in a multimachine power system. In order to model the system more efficiently, both steady-state and transient data are considered. An augmented data matrix in the Hankel structure is passed as an input to the DMD algorithm for capturing the behavior of the system accurately. Incorporating the persistency of excitation and Hankel structure in DMD, a rank condition is derived to identify the coherency of generators in post-fault situations. The proposed coherency identification dynamic mode decomposition algorithm is verified and validated on a 22-bus 6-generator system.

Coherency Identification in Multimachine Power Systems Using Dynamic Mode Decomposition

Grid synchronisation plays a vital role in grid tied inverters where the phase, frequency and voltage amplitude of grid is a critical information. In order to extract this information accurately, an improved technique of single-phase synchronous reference frame phase locked loop (SRF-PLL) is used in grid tied inverters. This method uses a transport delay block and a changed Park transform in such a way that the overall system performs well in frequency varying and harmonically distorted grid. The purpose of this work is to employ the model based development (MBD) approach on improved SRF-PLL algorithm, in order to automatically generate production quality code, increase productivity and shorten the development time of grid synchronisation module in grid tied inverters. The efficiency of proposed development approach is verified, tested and validated using off-line MATLAB/Simulink simulation, hardware-in-loop (HIL) real time simulation, software-in-loop (SIL) simulation. Hardware implementation and simulation results show potential application of MBD applied to improved SRF-PLL in grid tied inverter for synchronisation.

Implementation of single-phase modified SRF-PLL using model based development approach

The power system is operated to an increasing extent bordering its limits. Violation of these limits leads the system towards its bifurcation point (BP), resulting in voltage collapse. Therefore, tracking of BP for generator outage condition is essential, which gives a stability margin to the system operator for preventive control. Hence, a power flow solution, independent of convergence issue is of a great deal. This paper proposes a nested holomorphic embedding power flow method (HEPM) for obtaining power flow solution if one exists along with the bifurcation point and unambiguously notifies its nonexistence under sequential generator outage. It is devoid of the issues faced by iterative methods implemented during major blackouts, cascade failure, and voltage collapse situations. The complexity of the nested HEPM for calculation of the bifurcation point in terms of the number of power flow equations to be solved is extremely lower compared with other iterative methods. The proposed methodology is validated with a modified IEEE 14 bus system using MATLAB in the worst-case scenario where all generators fail except the slack bus.

K. Sonam

Papers

Gaussian process surrogate model for an effective life assessment of transformer considering model and measurement uncertainties

Finite control set model predictive control for two level inverter with fixed switching frequency

Coherency Identification in Multimachine Power Systems Using Dynamic Mode Decomposition

Implementation of single-phase modified SRF-PLL using model based development approach

Bifurcation Point Tracking in Generator Outage Scenario using Nested Holomorphic Embedding Power Flow Method