This paper proposes a dual-function dynamic voltage restorer (DFDVR) with fault current-limiting capability. The new topology can operate under different modes: the short-circuit fault current-limiting mode and the compensation mode for voltage fluctuation. Different functions of the DFDVR are achieved by controlling a brake chopper circuit and anti-parallel thyristors. During fault conditions, the anti-parallel thyristors are deactivated to protect the DC-side capacitor, and the duration of the on- and off-periods of the semiconductor switch in the brake chopper circuit are controlled to adjust fault current amplitudes. During voltage sags, the anti-parallel thyristors are activated, and the brake chopper circuit is turned off by controlling the gate turn-off thyristor (GTO) in the brake chopper circuit, thus making the DFDVR similar to a conventional DVR. The control scheme and optimal parameter selection of the DFDVR are provided. The design methodology of the DFDVR was verified through simulations performed using the PSCAD/EMTDC software. The experimental results prove the capability and effectiveness of the DFDVR topology.

The notion of cyber-physical power grid is to use metering, communication, and control to make an intelligent and autonomous power system. Therefore, the smart meters form the basis of the cyber-physical system. Recently, an increasing number of synchrophasor measurement units (PMU) and micro synchrophasor measurement units (μPMU) have been deployed in transmission grids and distribution networks, respectively. Because of the importance of these data, it is imperative to guarantee its integrity and authenticity. In this paper, the measurement structure and data format of the synchrophasor data are analyzed, which reveals the vulnerability of PMU data to cyber-attacks. Then the signal separation method, i.e. independent component analysis (ICA) algorithm, and its behavior are investigated, which reveals the mechanism of the PMU data attacks. Further, a Cognitive Radio (CR) based secure network architecture is propose as a countermeasure. With the stochastic but confined choice of the bandwidth and an unfixed number of Splepian tapers, the proposed architecture makes the data stream much harder to intercept by dynamically exploiting the unused PU band. The effectiveness of the proposed network is validated by the simulation results.

Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project.

The Automatic Generation Control (AGC) system is vital for power system frequency stability. The frequency and tie-line power flow are measured and transmitted to the control room to form Area Control Error (ACE), which is then sent to each generator for power generation adjustment. Due to the vulnerability of the Inter-Control Center Communication (ICCP) protocol, which is used for data transmission, many attacks such as Denial of Service, timing de-synchronization, and False Data Injection (FDI) attack can be inflicted upon the compromised system. In this paper, we investigated the attacking mechanism and the impact of the coordinated FDI attack. Compared with the single attack model, the coordinated FDI attack has a smaller Time to Emergency (TTE) value and wider parameter ranges. Therefore, it is stealthier and more harmful to the AGC system. However, it is found that the pattern of the corrupted ACEs (attacked by a specific coordination FDI attack) follows a specific fashion. Therefore, we proposed a self-learning and evolving approach to detect this stealthy attack. The real data from an electric company helps to train and test the pattern recognition model. The coordinated attack is simulated and compared in a 3-area AGC system, while the proposed detection method is verified via the IEEE 39-bus test system.

The distribution grid is undergoing profound changes nowadays, and state estimator has become an essential part in the control room to enable future smart grid. The newly deployed micro phase measurement units ( $\mu $ PMU) and advanced metering infrastructure (AMI) devices make the real-time state estimation possible for distribution system. Therefore, this paper studies the incorporation and process of the multi-source measurements and proposes a fast three-phase state estimation based on the hybrid measurements scheme. By the process of $\mu $ PMU measurements, pseudo voltage measurements are added, which significantly increases the estimation redundancy. For AMI measurements, the harmonic components model is used to overcome the asynchronicity problem. An improved sequential state estimation based on the changed measurements is proposed to enable a fast estimation. IEEE 13-node and 390-node systems are simulated to verify the efficacy and efficiency of the proposed method.

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.