International Journal of Electrical Power & Energy Systems最新文献

The characterization of the competitive interdependence among power consumers in power trading system contributes to pursuing efficient power supply and suppressing peak–valley load differences. Even though Stackelberg–Nash game approach has been applied to retail electricity trading with a single utility company and a group of end-users under competitive interdependence, the extension to multi-cluster retail electricity markets is not focused yet and the best strategies of end-users are still decoupled to each other. To bridge this gap this paper investigates the price-based demand response problem in multi-cluster retail electricity markets to coordinate the energy consumption behavior of the end-users and the retail price behavior of the utility companies. Adopting the structure of hierarchical game theory, the cluster retail price is controlled by the utility company to affect the energy consumption demand of end-users within the corresponding cluster and hence increase the income of both the utility companies and the end-users. In order to describe the coupled competition relationship among the bottom end-users in the system, the Cournot competition model of the differentiated oligopoly market is constructed by taking energy consumption as the individual strategy of end-users. The existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system are proved, and a seeking algorithm is proposed to seek the Stackelberg Nash equilibrium, which is regarded as the extension from the existing algorithm for multi-cluster setup. This work gives an insight to theoretically check whether the utility companies need to redesign the contract with the power generator to ask for larger maximum supply.

Phasor Measurement Units (PMUs) are widely used in power system state estimation due to its high sampling rate and real-time estimation of power state in power systems. However, it is also vulnerable to cyberattacks (GPS spoofing attacks) since it uses unencrypted GPS civil signals for synchronization. The GPS spoofing attacks (GSAs) will intentionally manipulate the time reference of PMUs, which is equivalent to falsifying the phase angle of the PMU measurements. In this paper, we consider the secure power state estimation and attacks detection with the unknown GSAs in the power systems. Particularly, we derived the PMU-based GSAs model in the power state estimation problem, which is formulated as a mixed maximum likelihood problem. In the formulated problem, the involved unknown parameters are coupled and the objective function is non-convex, which are of significant challenges in finding optimal solutions. To tackle these challenges, a joint maximum a posteriori and maximum likelihood (JMAP-ML) algorithm is proposed to securely estimate the power state and detect the GSAs in the mixed measurements of PMUs. Different testing scenarios in IEEE 14 bus system are simulated to show the proposed algorithm’ performance on GSAs detection and state estimation. Numerical examples demonstrate the improved accuracy of our algorithm compared with classical algorithms when GSAs are present. And we conclude that when a sufficient number of PMUs are deployed in the system, the impact of GSAs will be largely compensated in the estimation stage.

With large-scale grid integration of renewable energy sources (RES), power grid operations gradually exhibit the new characteristics of high-order uncertainty, leading to significant challenges for system operational security. Traditional model-driven generation dispatch methods require large computational resources, whereas the widely concerned Reinforcement Learning (RL)-based methods lead to issues such as slow training speed due to the high complexity and dimension of processed grid state information. For this reason, this paper proposes a novel Grid Expert Strategy Imitation Learning (GESIL)-based real-time (5 min intervals in this paper) dispatch method. Firstly, a grid model is established based on the graph theory. Secondly, a pure rule-based grid expert strategy (GES) considering detailed power grid operations is proposed. Then, the GES is combined with the established model to obtain a GESIL agent using imitation learning by offline–online training, which can produce specific grid dispatch decisions for real-time. By designing a graph theory-based grid model, a model-driven purely rule-based GES, and embedding a penalty factor-based loss function into IL offline–online training, GESIL ultimately achieves high training speed, high solution speed, and strong generalization capability. A modified IEEE 118-node system is employed to compare the proposed GESIL to traditional dispatch method and RL method. Results show that GESIL has significantly improved computational efficiency by approximately 17 times and training speed by 14.5 times. GESIL can more stably and efficiently compute real-time dispatch decisions of grid operations, enhancing the optimization effect in terms of transmission overloading mitigation, transmission loading optimization, and power balancing control.

The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability.

Line-commutated converter-based high voltage direct current transmission (LCC-HVDC) has been widely applied worldwide due to its great advantages in realizing long-distance and large-capacity power transmission. However, it may also lead to instability problems, including subsynchronous torsional interaction (SSTI). This destructive phenomenon greatly threatens the safe and stable operation of power systems and has been widely concerned since the 1970 s. Existing literature has found that SSTI is caused by DC current control of rectifier station. However, the physical mechanism of this phenomenon has not been clarified clearly, which hampers further understanding of how negative damping is generated and whether it is evitable. To fill this gap, the physical process of SSTI caused by LCC-HVDC is clarified through the perspective of vector synchronization. Based on this, the negative damping mechanism is revealed. The influence of control on damping is also studied with the contribution of different control loops quantified. All results are verified through time-domain simulations and damping torque analysis.

Abstract

The valve Reactor (VR) is an indispensable protective element within High Voltage Direct Current (HVDC) system converters, susceptible to malfunctions such as overheating and localized deformation under the conditions of ultra-high frequency and discontinuous operational excitation. This work provides a comprehensive review of the latest research findings on the electrical, thermal, and vibrational properties of VR. Initially, this work utilizes circuit models to analyze the constituent components of various VR types and their electromagnetic properties under actual working conditions, with a profound exploration of the insulation technologies involving epoxy resin and insulated silicon steel coatings. Subsequently, this work incorporates finite element analysis (FEA) on the basis of loss calculation models and conventional thermal circuit models to explain the temperature properties of VR from a multi-physics perspective. Furthermore, this work integrates mechanical calculation models with FEA models to elaborate on the vibrational properties of VR under practical excitations and discusses optimization measures. Finally, this work envisions the future development directions of VR from the perspectives of electrical, thermal, and vibrational properties, and proposes optimization suggestions, providing valuable references for the improvement of VR design and condition evaluation.

3D printing forming quality control is a research hotspot and difficulty. The traditional approach is either to optimize the printing material properties, to optimize the printing device structure, or to control the printing process, for achieving the purpose of precision printing. In fact, in the 3D printing control system (3DPCS), control and measurement signals are transmitted through open communication network, inevitably leads to communication and transmission delays, which can adversely affect the 3DPCS dynamics and even cause instability, not to mention guarantee precision printing. For such a time-delay system, this paper presents an open issue, i.e. the stability analysis and stabilization control problem of the 3DPCS, in order to serve the improvement of the printing accuracy. First, the 3DPCS framework and dynamic model with time-varying delay are proposed. Second, the delay-dependent stability and stabilization conditions of the 3DPCS are derived by constructing the augmented Lyapunov–Krasovskii functional (LKF) and by using the relaxed mixed convex combination technologies, after which less conservative conditions are obtained by introducing a free weighting matrix to improve the accuracy. Thus, the corresponding controller gain is further obtained. Finally, the 3DPCS example and a well-known numerical example are carried out. Simulation results show that the upper bound of acceptable time delay of systems are larger, and the controller designed based on the stabilization condition can ensure the stable operation of the 3DPCS. Both aspects demonstrate the advantages of the proposed approach.

Power transformer diagnostic methods based on traditional intelligent learning are affected by the scarcity of transformer fault data, which hinders their further application and prevents them from obtaining high diagnostic accuracy. To solve this problem, a few-shot method based on Gaussian Prototype Network (GPN) is proposed to achieve an effective and accurate diagnosis of power transformers using even a small number of fault samples. The method is an organic combination of embedding network and distance metric. The proposed approach is verified by datasets of dissolved gas and literature, which come from real power transformers and historical data. The results show that the method can achieve up to 96.7% accuracy, which is suitable for the field of power transformer fault diagnosis.

This work presents a technique to estimate on-line the global inertia of an electric power system by exploiting the footprint of the principal frequency system dynamics. This method can estimate the inertia provided as a whole by synchronous machines, as well as by converter-interfaced generators controlled to emulate the behavior of the former through virtual inertia. Probing tones are injected by a grid-forming converter-interfaced generator and its virtual rotor speed is used to extrapolate its footprint. As a result, the method requires neither measuring the active power exchange of each synchronous generator nor extrapolating their rotor speeds. Since the proposed technique is entirely data driven, it does not require any model of the power system generators/prime-movers/controllers and of the interconnecting grid. The method is comprehensively tested on a modified version of the IEEE 39-BUS system and a dynamic version of the IEEE 118-BUS system, both containing grid-forming converter-interfaced generators.

Commercial virtual power plant is one of the most efficient means to solve the problem of multi-type distributed power supply and flexible load scheduling. Aiming at the problems of uncertain renewable energy sources and reliable distribution system, a flexible interactive coordinated control method for commercial virtual power plants based on worst-case conditional value-at-risk (WCVAR) is proposed in this paper. In order to reduce the risk of voltage exceeding limits and increased network losses at distribution network nodes, combined with the requirements of technology virtual power plants for power flow control technology in distribution networks, a commercial virtual power plant net profit model based on the reliability of distribution network operation is established. The worst-case conditional risk value theory is used to quantify the risks caused by the uncertainty of wind, water and photovoltaic outputs. A commercial virtual power plant optimization scheduling model based on the WCVAR is established with the goal of minimizing risk cost, and the model is solved by commercial software Cplex. By simulating and analyzing the actual operating data of the improved IEEE-33 distribution system, the output characteristics of commercial virtual power plants under different risk confidence levels are compared and analyzed. The simulation results show that this method fully explores the scheduling potential between electricity, power grid, load and energy storage in commercial virtual power plants, flexibly adjusts risk preferences according to scheduling needs, and achieves flexible control of benefits and risks in commercial virtual power plants.