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This paper proposes a decision tree (DT)-based systematic approach for cooperative online power system dynamic security assessment (DSA) and preventive control. This approach adopts a new methodology that trains two contingency-oriented DTs on a daily basis by the databases generated from power system simulations. Fed with real-time wide-area measurements, one DT of measurable variables is employed for online DSA to identify potential security issues, and the other DT of controllable variables provides online decision support on preventive control strategies against those issues. A cost-effective algorithm is adopted in this proposed approach to optimize the trajectory of preventive control. The paper also proposes an importance sampling algorithm on database preparation for efficient DT training for power systems with high penetration of wind power and distributed generation. The performance of the approach is demonstrated on a 400-bus, 200-line operational model of western Danish power system.

This paper proposes a novel method for power system dynamic simulation that solves power system differential algebraic equations by a semi-analytical and semi-numerical approach using continued fractions. The method implements a two-stage scheme to enhance online performance of simulation: the offline derivation stage finds approximate analytical solutions, so-called “semi-analytical solutions,”for state variables of dynamic devices, such as generators in the form of power series of time with symbolic coefficients about system conditions; the online evaluation stage substitutes values on actual system conditions for symbolic coefficients, then transforms the solution into a continued fraction to prolong its time interval of accuracy, and finally calculates the system's trajectory over consecutive, adaptive time intervals for expected simulation results. A priori error bound for continued fractions is proposed to enable the simulation on adaptive time intervals. Compared with the conventional numerical simulation methods, the proposed continued fraction-based method has a fast simulation speed and a good suitability for parallel computing. The method is demonstrated and tested on the IEEE 9-bus system, the IEEE 39-bus system, and Polish 327-generator 2383-bus system.

This paper proposes a new remote voltage control approach based on the non-iterative holomorphic embedding load flow method (HELM). Unlike traditional power-flow-based methods, this method can guarantee the convergence to the stable upper branch solution if it exists and does not depend on an initial guess of the solution. Bus type modifications are set up for remote voltage control. A participation factor matrix is integrated into the HELM to distribute reactive power injections among multiple remote reactive power resources so that the approach can remotely control the voltage magnitudes of desired buses. The proposed approach is compared with a conventional Newton-Raphson (N-R) approach by study cases on the IEEE New England 39-bus system and the IEEE 118-bus system under different conditions. The results show that the proposed approach is superior to the N-R approach in terms of tractability and convergence performance.

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. Revised and Submitted to IEEE Transaction on Power Systems

This paper proposes a new analytical probabilistic power flow (PPF) approach for power systems with high penetration of distributed energy resources. The approach solves probability distributions of system variables about operating conditions. Unlike existing analytical PPF algorithms in literature, this new approach preserves nonlinearities of ac power flow equations and retain more accurate tail effects of the probability distributions. The approach first employs a multidimensional holomorphic embedding method to obtain an analytical nonlinear ac power flow solution for concerned outputs such as bus voltages and line flows. The embedded symbolic variables in the analytical solution are the inputs such as power injections. Then, the approach derives cumulants of the outputs by a generalized cumulant method, and recovers their distributions by Gram-Charlier expansions. This PPF approach can accept both parametric and nonparametric distributions of random inputs and their covariances. Case studies on the IEEE 30-bus system validate the effectiveness of the proposed approach.

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is setup based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of a faster convergence rate with matched solution qualities. This paper has been accepted by IEEE Transactions on Sustainable Energy and now still in online-publication phase, IEEE Transactions on Sustainable Energy. 2018