ABSTRACT. The new round of Internet era will completely change the way of energy consumption and greatly improve the efficiency of energy utilization. It is imperative to integrate the energy industrial Internet with the widely known consumption Internet. In order to improve energy efficiency and introduce market competition mechanism, the concept of Energy-Use Internet (EUI) has been proposed in this paper. Within the scope of EUI, energy suppliers, energy service providers, energy aggregators and energy consumers are collectively referred to as energy elements. These energy elements with trading and circulation functions are regarded as the basis of EUI. Using the Internet thinking for reference, a fair and free energy service and trading platform needs to be constructed. The friendly interaction mechanism between EUI and power grid has been extensively studied, mainly focusing on interaction mode, business mode, and regulation from grid company. Finally, some key technologies to realize the scenario of EUI and interaction with power grid are discussed.

ABSTRACT. Due to the advantages of dry-type transformers such as safety, no pollution, and low power consumption, they are widely used in shopping malls, hospitals, data centers and other places. Therefore, anomalous state detection for dry-type transformers is of great significance. However, the traditional detection methods are generally based on the hard threshold judgment method, which is difficult to ensure timeliness and may cause irreversible damage to the device. In this paper, we present dTASD, Dry-type Transformer Anomalous State Detector, a framework that can timely detect the anomalous state of dry-type transformer online. dTASD consists of an offline training model stage and online detecting stage. In offline training model, dTASD adopts the semi-supervised mode, and applies self-organizing map to discretize the three-phase temperature data to solve the challenge of three-phase data fusion. In online detecting, we propose a novel calculation method for anomaly scores to measure the degree of transformer operation deviating from the normal state. The experimental results using Real monitoring data of dry-type transformers installed in a large data center demonstrate dTASD can effectively solve the problem of anomalous state detection for dry-type transformers, and outperforms the existing anomaly detection approaches.

ABSTRACT. With the development of the Energy Internet (EI), the application of smart grids has expanded from the industrial field to homes and individuals, which effectively promotes the development of home Internet of Things (IoT) devices. The research of the home IoT aims to improve the user experience, and the focus is on the intelligence of the device. The intelligence of the device is inseparable from human-computer interaction (HCI). Speech emotion recognition(SER) uses machines to recognize emotions in human speech, which is an important part of HCI. However, noise usually greatly influences the recognition accuracy in real HCI. In this paper, we conducted experiments on 16 types of common noise in the environment. We find that some types of noise influence the recognition effect significantly but some do not. We explained this difference from two perspectives—spectrogram and statistical characteristics so that we will gain more insight as to what type of noise will and/or will not influence the recognition accuracy of a particular SER task.

ABSTRACT. Active suspension uses a powered actuator to provide real-time control of a suspension system to achieve better ride, comfort, and safety for passengers in a vehicle. This study concerns with the design of control schemes for an active suspension system in a quarter car model. In this paper, a quarter car model is presented, and ISO-based road profiles are used as perturbation for the system. Two control strategies, LQR and MPC with reference tracking have been investigated. Quadratic cost function for both the control schemes is optimized for the state and input variables. Simulation is carried out using MATLAB-SIMULINK and a comparison is presented for the ride index and actuator power. Simulations show considerable improvements in the suspension performance and power demand using MPC in comparison with LQR on two road classes. The performance improvements using MPC provides substantial evidence that indicates a reduction in the power requirements, actuator dimension and weight.

ABSTRACT. Accurate transmission line (TL) parameters are the basis of power system calculations. In recent years, artificial intelligence (AI) develops rapidly, which has been applied widely in power systems. However, AI is rarely applied to TL parameter identification. Thus, combining the TL model and AI, this paper proposes a robust identification method for TL parameters combined with BP (back propagation) neural network and median robust estimation, with the modified SCADA measurements based on TL model. Specifically, first, the robust identification method for TL parameter combined with BP neutral network and median estimation is proposed. And then, the training set that considers various working conditions and different line parameters is constructed based on the π-equivalent model. Furthermore, the input data of BP neural network is construed by modifying the SCADA data based on TL model. In addition, the median estimation used to obtain the final result, which could reduce the interference of noise, is introduced. Finally, the results with simulated data and measured SCADA measurements data show the effectiveness and practicality of the proposed method, respectively.

ABSTRACT. The construction of extra-large smart cities needs efficient and energy-efficient rail transit infrastructure to provide smart and eco-friendly life. In order to improve the planning and design level of urban rail transportation and realize the recovery and reuse of train traction braking energy. Combined with the digital twin technology, this paper analyzed the characteristics of various components in the traction power supply AC-DC hybrid network, including the rectification and inverter characteristics, the charge and discharge characteristics of the energy storage device, and the impact of the train converter on the system. Their mathematical models were constructed. A source-network-load-storage interaction model was established in MATLAB/Simulink environment. Finally, a three-stations between two-intervals model was built, which can simulate the normal operation of the system. Based on the voltage protection strategy settings, the stability of the system operation is verified.

ABSTRACT. With the rapid progress of informatization construction in power business, data resource has become the basic strategic resource of the power industry and innovative element in power production. The security protection of data in power business is particularly important in the informatization construction of power business. In order to implement data security protection, transparent encryption is one of the fifteen key technical standards in the Construction Guideline of the Standard Network Data Security System. However, data storage in the encrypted state is bound to affect the security audit of data to a certain extent. Based on this problem, this paper proposes a scheme to audit the sensitivity of the power business data under the protection of encryption to achieve an efficient sensitivity audit of ciphertext data with the premise of not revealing the decryption key or data information. Through a security demonstration, this paper fully proves that this solution is secure under the known plaintext attacks