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Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Reliability evaluation of phasor measurement unit (PMU) is a primary key element in the reliability evaluation of wide-area monitoring system (WAMS). In this paper, a comprehensive reliability evaluation method based on Monte Carlo dynamic fault tree (MCDFT) analysis is proposed to conduct the reliability evaluation on PMU. The reliability model of PMU is constructed using dynamic fault tree modeling and analyzed using Monte Carlo simulation to evaluate the reliability indices of PMU. The validity and advantages of the proposed MCDFT reliability evaluation of PMU were verified with simulation and comparison studies. Importance analysis showed that basic components in the GPS receiver and CPU hardware modules have high impacts on the reliability of PMU. Sensitivity and redundancy design analysis are then applied to conclude that the redundancy design of GPS receiver and CPU hardware would be the best measure for improving the reliability of PMU. Finally, a self-adaptive wide-area damping control scheme is taken as an example for the application of PMU reliability.

A reduced-order, “grey-box” model of an active distribution network, intended for dynamic simulations of the transmission system, is derived. The network hosts inverter-based generators as well as static and motor loads, whose dynamic parameters are affected by uncertainty. This issue is addressed using Monte-Carlo simulations. The parameters of the equivalent are adjusted to match as closely as possible the average of the randomized responses, while their dispersion is accounted for through the weights of the weighted least-square minimization. A procedure is used to remove from the identification the parameters with negligible impact. To avoid over-fitting, the equivalent is tuned for multiple large-disturbance simulations. A recursive procedure is used to select the smallest possible subset of disturbances involved in the least-square minimization. Simulation results with a 75-bus MV test-system are reported. They show that the equivalent is able to reproduce with good accuracy the discontinuous controls of inverter-based generators, such as reactive current injection and tripping.

Photovoltaic (PV) systems play an important role in contemporary electricity production as a ubiquitous renewable energy source. However, the performance of a PV system is susceptible to unexpected faults that occur inside its various components. In this paper, we propose a quickest fault detection algorithm for PV systems under the sequential change detection framework. In particular, multiple meters are employed to measure different output signals of the PV system. The time correlation of the faulty signal and the signal correlation among different meters are exploited by a vector AR model in modeling the post-change signal. In order to tackle the difficulty that no prior knowledge about the fault is available, we develop a change detection algorithm based on the generalized local likelihood ratio test. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and fast detection in dealing with various types of faults in PV systems.

This paper investigates the possible cyber-physical attacks on voltage stability monitoring of a power transmission system. By considering a smart adversary that can launch a vector attack based on power flow equations, conventional voltage stability monitoring systems based on voltage stability indexes will fail to detect the instability issue. This can mislead the Power System Operator (PSO) to take inappropriate corrective action. In order to prevent and to combat such a malicious attack, a new indicator for efficient intrusion detection and a novel mitigation algorithm is proposed. This proposed detection algorithm employs the multi-port equivalent circuit of the power system to calculate the Thevenin Equivalent (TE) parameters. These TE parameters are then used to calculate an indicator, which reveals the attack on Phasor Measurement Unit (PMU) data. The proposed mitigation scheme then helps PSO to detect which PMU is under-attack. Furthermore, the amount of injected attack vectors is computed or estimated depending on the number of compromised PMUs. The effectiveness of the proposed techniques is demonstrated via illustrative case studies.

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

$Q$ -learning-based operation strategies are being recently applied for optimal operation of energy storage systems, where, a $Q$ -table is used to store $Q$ -values for all possible state-action pairs. However, $Q$ -learning faces challenges when it comes to large state space problems, i.e., continuous state space problems or problems with environment uncertainties. In order to address the limitations of $Q$ -learning, this paper proposes a distributed operation strategy using double deep $Q$ -learning method. It is applied to managing the operation of a community battery energy storage system (CBESS) in a microgrid system. In contrast to $Q$ -learning, the proposed operation strategy is capable of dealing with uncertainties in the system in both grid-connected and islanded modes. This is due to the utilization of a deep neural network as a function approximator to estimate the $Q$ -values. Moreover, the proposed method can mitigate the overestimation that is the major drawback of the standard deep $Q$ -learning. The proposed method trains the model faster by decoupling the selection and evaluation processes. Finally, the performance of the proposed double deep $Q$ -learning-based operation method is evaluated by comparing its results with a centralized approach-based operation.

Smart grids have attracted increasing interest in the field of industrial research. These systems allow electricity consumers to communicate with electrical systems using bidirectional communications. However, these bidirectional channels may introduce privacy threats to consumers. Considerable research has been conducted with the aim of resolving these threats. Unfortunately, a practical smart grid design with adequate efficiency and security functionalities has not yet been produced. In this paper, we propose lightweight privacy preserving metering protocols by designing a distributed authentication method to further increase the speed of the message authentication process. We confirmed the efficiency and security of our method by performing a detailed analysis.