Wind farms commonly cluster in regions rich in wind resources. Thus, correlation of wind speeds from different wind farms should not be ignored when modeling a power system with large wind energy penetration. This paper proposes a probabilistic optimal power flow (POPF) technique based on the quasi-Monte Carlo simulation (QMCS) considering the correlation of wind speeds using copula functions. In this paper, a copula function is used to model the dependent structure of random wind speeds and their forecast errors. QMCS is employed in the sampling procedure to reduce computation burden. The proposed method is applied in probabilistic power flow (PPF). Furthermore, the PPF is used in the POPF problem that aims at minimizing the expectation and downside risk of fuel cost simultaneously. Simulation studies are conducted on a modified IEEE 118-bus power system with wind farms integrated in two areas, and the results show that the accuracy and efficiency are improved by the proposed method.

This paper presents a perturbation estimation based robust load frequency control (LFC) scheme for power system subject to random time-delay attack. Perturbation term is defined to describe the measurement deviation caused by random time-delay attack. Besides, perturbation observer is designed to estimate the system perturbation. The load frequency controller is compensated based on the perturbation estimation to mitigate the random time-delay attack and ensure the stable operation of power system. Case studies are undertaken on a two-area LFC model to demonstrate the control performance of the scheme.

Effective fault diagnosis for power electronic converters is an essential and mandatory operation to reduce failures and unscheduled shutdowns. This paper presents a datadriven fault diagnosis method, using long short-term memory (LSTM) network, to detect multiple open-circuit switch faults of the back-to-back converter in doubly-fed induction generator (DFIG)-based wind turbine systems. Twelve sensor signals of the back-to-back converter for different fault types are measured. Wind speed fluctuation and sensor bias faults are considered as interference factors. The method has been evaluated in a grid-connected DFIG simulink model. Simulation results have demonstrated that, compared with least squares support vector machine (LS-SVM), back-propagation artificial neural network (BPANN) and recurrent neural network (RNN), the proposed method excavates the deep information of the fault signal with the highest diagnosis accuracy and strongest robustness with short time delay. Experimental tests undertaken on a hardware-in-theloop testing platform further validate the effectiveness of the LSTM-bsed network.

In this paper, a new topology of two-stage cascaded switched-diode (CSD) multilevel inverter is proposed for medium-voltage renewable energy integration. First, it aims to reduce the number of switches along with its gate drivers. Thus, the installation space and cost of a multilevel inverter are reduced. The spike removal switch added in the first stage of the inverter provides a flowing path for the reverse load current, and as a result, high voltage spikes occurring at the base of the stepped output voltage based upon conventional CSD multilevel inverter topologies are removed. Moreover, to resolve the problems related to dc source fluctuations of multilevel inverter used for renewable energy integration, the clock phase-shifting (CPS) one-cycle control (OCC) is developed to control the two-stage CSD multilevel inverter. By shifting the clock pulse phase of every cascaded unit, the staircase-like output voltage waveforms are obtained and a strong suppression ability against fluctuations in dc sources is achieved. Simulation and experimental results are discussed to verify the feasibility and performances of the two-stage CSD multilevel inverter controlled by the CPS OCC method.

With increasing deployment of distributed energy resources, the energy market which aims for local generation and load profile redistribution is facing the challenge to accommodate various types of participants. To realize social welfare maximization with privacy preserving in a dynamic energy market, this article propose a multiagent reinforcement learning (MARL) method for quotation decision optimization in continuous double auction (CDA)-based peer-to-peer (P2P) energy market. To address the nonstationarity and privacy violation brought by multiagent context, we utilize mean-field approximation to abstract the unauthorized local information of other agents from the public market dynamics. An abstract Q-value function is developed for each agent to infer the neighbor agents' local observation and action through the public clearing results in the dynamic CDA market. Moreover, to avoid sparse reward so as to stabilize the learning process, we propose a dynamic potential-based reward shaping term in the reward. Without altering the learnt optimal policies, the agents can be informed with the additional energy storage state as the reward shaping in each time instants. To validate the effectiveness and economy of our proposed method, simulation studies are conducted on a real-world dataset. Simulation results show that the proposed MARL method produces up to 17% more convergent episodic reward and 67% less energy bills which indicates competitive convergence performance and significant economic benefits.

In order to improve the reliability of wind turbines, avoid serious accidents and reduce operation and maintenance costs, it is important to effectively detect early faults of wind turbines operating in harsh environment. This paper proposes a data-driven fault diagnosis and isolation (FDI) method for wind turbines, which implements Long Short-Term Memory (LSTM) networks for residual generator and applies the random forest (RF) algorithm for decision making. The method has been evaluated in a wind turbine benchmark simulink model proposed in the literature, in comparison with four model-based algorithms and four data-driven methods, and the results have shown that the proposed method achieves the highest accuracy. Moreover, extensive evaluation has been conducted to analyse the robustness of proposed method, and the experimental results have verified the stability of the proposed method in diagnosis of wind turbine faults.