Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

Optimal Control of Microgrids with Multi-stage Mixed-integer Nonlinear Programming Guided Q-learning Algorithm

This paper proposes an energy management system(EMS)for the real-time operation of a pilot stochastic and dynamic microgrid on a university campus in Malta consisting of a diesel generator,photovoltaic panels,and batteries.The objective is to minimize the total daily operation costs,which include the degradation cost of batteries,the cost of energy bought from the main grid,the fuel cost of the diesel generator,and the emission cost.The optimization problem is modeled as a finite Markov decision process(MDP)by combining network and technical constraints,and Q-learning algorithm is adopted to solve the sequential decision subproblems.The proposed algorithm decomposes a multi-stage mixed-integer nonlinear programming(MINLP)problem into a series of single-stage problems so that each subproblem can be solved by using Bellman’s equation.To prove the effectiveness of the proposed algorithm,three case studies are taken into consideration:(1)minimizing the daily energy cost;(2)minimizing the emission cost;(3)minimizing the daily energy cost and emission cost simultaneously.Moreover,each case is operated under different battery operation conditions to investigate the battery lifetime.Finally,performance comparisons are carried out with a conventional Qlearning algorithm.