Abstract:Flexible reserve capacity support is important for mitigating active power imbalance issues in asynchronous power systems. Electrolytic aluminum loads (EALs), owing to their large capacity and rapid response, are used as regulation resources in this study. Combining EALs with renewable energy generation units allows the sending power system to provide active power support to the receiving power system when a large power disturbance occurs. An active power support control strategy is proposed for a voltage source converter based high voltage direct current (VSC-HVDC) asynchronous power system. The active power control method of the EAL is analyzed as the foundation, and the load frequency control models of the sending and receiving systems are presented to promote the proposed control strategy. Active power controllers based on model pre-dictive control (MPC) theory are designed to manage power system uncertainties and external disturbances. The proposed active power support control strategy is realized by optimizing the regulation resources in the sending power system while maintaining a stable frequency when the reserve capacity of the receiving system is insufficient. An actual industrial power grid with renewable energy is selected as the sending system and simulations are performed to verify the effectiveness of the proposed active power support control strategy and MPC-based controllers.

Abstract:Recent developments in agent-based systems provide an effective solution to many operational problems of power systems. This paper proposes a protection scheme that uses agent-based relays in a cooperative multiagent structure. The relay primarily starts as an overcurrent relay to detect the fault. Then, it starts data exchange with one peer relay in case of line faults or few neighboring relays in case of busbar faults to confirm the fault location. At this later step, it acts as a differential relay that compares the current phasors at both line ends in the case of line faults and computes the net outgoing current in the case of busbar faults. The scheme design is presented, and the agent cooperation protocol is described. To enforce the scheme against false data injected by hackers via intruding communication facilities, an anomaly detection device is prepared and integrated into each agent. The proposed tool is based on a one-class support vector machine and can firmly discriminate real fault data from injected false data. The tool also enables the relay to recognize the challenging high impedance fault. The proposed method is tested by dynamic simulation on the IEEE 9- and 39-bus systems under various conditions. The performance is evaluated by comparing it with that of other recent techniques.

Abstract:A mismatch between the rapid growth of grid-connected offshore wind farms (OWFs) and the delay of onshore transmission expansion (OTE) planning has caused significant wind curtailment in power systems. To alleviate this problem, a coordinated planning (CP) model of multiple inner networks of OWFs (INOWFs) and OTE is established in this paper. In the proposed CP model, the explicit connection topology constraints of a new double-sided ring topology for INOWFs are defined, and the connection topologies and electrical components of both INOWFs and OTE are coordinately optimized. In addition, a parallel random search method is proposed to obtain the set of feasible connection topology schemes of each INOWF, which can contract the feasible region of the CP model and reduce the difficulty in obtaining the optimal solution. Finally, a decomposition and coordination planning algorithm is developed to decompose the original complex optimization model into two tractable sub-models for INOWF and OTE planning. This approach efficiently obtains the optimal CP scheme for multiple INOWFs and OTE through alternating iterations. Case studies on the modified IEEE 39-bus system with two OWFs and an actual provincial power system with six OWFs demonstrate the correctness and efficiency of the proposed model and algorithm.

Abstract:The attack of false data injection can contaminate the measurements acquired from the supervisory control and data acquisition (SCADA) system, which can seriously endanger the safety and stability of power system operations. The conventional machine learning attack detection methods use a single strong classifier and are difficult to solve the problem of overfitting, making them lack of generalization ability. On the other hand, most existing dimension reduction approaches based on feature extraction can change the original physical meanings of measurements. Here, a novel method is proposed based on feature selection and ensemble learning to solve the above problems. Squirrel search algorithm combines Latin hypercube sampling and opposition-based learning to form an improved algorithm with strong global search ability for feature selection. This avoids the problem of feature extraction changing the original physical meanings of measurements. Besides, the classifier based on adaptive boosting decision tree ensemble learning algorithm with stronger generalization ability is used to distinguish the false data injection. Simulation results using the IEEE 14-bus and IEEE 57-bus test systems verify the proposed method with higher performance of detection compared with other widely adopted methods.

Abstract:This study explores tower vibrations in large-scale permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). First, the aerodynamic characteristics of wind turbines, including wind shear (WS), tower shadow effect (TSE), and blade airfoil structure, are examined. Then, a mechanism model of tower vibration is established, and the impacts of WS and TSE on tower vibration are analyzed. Suppression schemes, including crossing resonance zone method and tower damping control, are evaluated, and a robust variable-pitch strategy based on sliding mode control is proposed to mitigate tower vibration. Comparative analysis suggests that the proposed strategy out-performs the crossing resonance zone method and the tower damping control in achieving more effective tower vibration suppression and reducing the influence of the 3p frequency component. The effectiveness of the model and algorithm is verified through simulation experiments.

Abstract:A safe and dependable supply of energy and power is directly correlated with the quality of distribution network engineering. The assessment and diagnosis of the design quality and economic viability of a distribution network engineering process are essential for guaranteeing the steady functioning of the corresponding power system. In this paper, an intelligent assisted assessment technique for distribution network engineering is proposed to address the issues of inefficiency, high manual dependence, and low utilization of vital information in the text during the evaluation of projects related to distribution network engineering. To improve the model's contextual learning ability, the robustly optimized bidirectional encoder representations from transformers pretraining approach and whole-word masking are adopted to extract useful features from the distribution network engineering project review text. Principal component analysis is then used to downscale the high-dimensional features, thereby greatly increasing the efficiency of downstream classification. The light gradient boosting machine performs classification on the downscaled text features, and the Bayesian optimization approach is utilized to identify the best hyperparameter combinations. This significantly lessens the impacts of random parameters on the model performance. Tenfold cross-validation results demonstrate that the model can quickly and accurately identify common problems in distribution network projects' technical and economic dimensions.

Abstract:Resilient enhancement measures are crucial for increasing systems' capacities to deal with extreme natural disasters. However, in the pre-disaster prevention stage of hurricanes, research that simultaneously considers load importance, vulnerable lines, and multiple resilience enhancement measures is lacking. To address this issue, a novel resilience-oriented transmission expansion planning (ROTEP) model is proposed that incorporates two resilience assessment indices: the combined loss of loads (CLL) and the vulnerable line survival proportion (VLSP). In addition, the novel function of the proposed model meets the requirements of normal and hurricane damage scenarios based on the collaborative implementation of three resilience enhancement measures (expansion planning, hardening, and unit commitment). The proposed ROTEP model is structured in two stages. The first-stage model aims to meet the load growth demand while minimizing the total planning cost of transmission lines, the operating cost of generators, and the penalty cost of wind power and load shedding across several normal scenarios. Based on the scheme obtained from the first-stage model, damage scenarios are constructed, and a fault chain set is formulated using a hurricane simulation model. Then, a cascading fault graph is constructed to identify vulnerable lines. The second-stage model further enhances the CLL and VLSP (if necessary) under several damage scenarios by hardening the highest-contributing or most vulnerable line. Finally, the efficacy of the proposed ROTEP model for enhancing resilience is validated with a modified IEEE RTS-24 system and a two-area IEEE RTS-1996 system.

Abstract:In the context of increasing demand for flexibility and controllability in distribution networks, this paper proposes a dual-bus parallel supply (DBPS) system based on bipolar direct AC/AC conversion. First, the topology of the DBPS system, which is composed of both conventional and flexible bus ports, is described. Then, through analyzing the principles of the DBPS system, the voltage flexible regulation range is obtained, and its superiority over a topological reciprocity system is achieved. Then, a control strategy for the DBPS system is proposed based on the theory of power flow regulation of distribution ring networks and the principles of flexible bus regulation of the DBPS system. Finally, simulation analyses on flexible control scenarios in different networking modes of the DBPS system verify the correctness and validity of the proposed theory.

Abstract:Dynamic temperature monitoring at critical locations of IGBT modules is a key means to improve the reliability of high-power converters. However, most existing thermal model-based methods suffer from temperature estimation errors due to model parameter variations and loss calculation errors. To address this problem, based on the reduced-order thermal model, an H∞ observer-based robust 3-D thermal monitoring method for IGBT modules is proposed in this paper. Through the optimized design of the observer feedback gain, the thermal model and real-time temperature information are effectively combined, which reduces the temperature estimation error in the worst case. Thus, the proposed method is more robust to model parameter uncertainty and loss error than the conventional temperature observers. Experiment validations of the proposed H∞ observer and conventional observers are provided. The results demonstrate that the proposed observer achieves the highest temperature estimation accuracy under various system uncertainties, making it an effective solution for reliable online thermal monitoring of IGBT modules over the whole life cycle.

Abstract:Recently, lithium-ion batteries (LIBs) have become the leading energy storage solution for electric vehicles due to their high energy density and long lifespan. Examining the health condition of LIBs is essential for their safe and reliable operation. This paper thoroughly assesses the latest researches on techniques for forecasting the health of LIBs, examines the properties of diverse methodologies, and proposes future development directions. First, the aging mechanism of lithium-ion batteries is introduced and the factors affecting battery aging are explored. Then, based on different prediction objectives, the prediction of lithium-ion battery health is divided into state of health (SOH) estimation and end of life (EOL) prediction. The SOH estimation methods are introduced from model-based and data-driven methodologies, while the EOL prediction is focused on the data-driven methods. Finally, the future development direction of LIB health prediction is identified, and four new potential topics on battery prediction are proposed.

Abstract:As carbon emissions reduction is becoming increasingly important for sustainable development and carbon neutrality targets, the concept of carbon emission market has been recently proposed in order to essentially manage carbon emission on the demand side by allowing electricity consumers to purchase or sell carbon emission quotas. Hence, real-time demand-side carbon emission monitoring (DCEM), indicating the amount of carbon emission that each electricity consumer should be responsible for, becomes a necessity for the operation of the carbon emission market. However, the real-time DCEM cannot be achieved by carbon emission flow (CEF) analysis due to the unavailability of real-time power demand data. In this connection, this paper proposes a two-stage real-time DCEM method based on the graph neural network (GNN). In the first stage, power system operating scenario data, including the power generation capacity and power demand data, are collected to calculate carbon emission patterns through CEF analysis. In the second stage, a data-driven GNN-based model is designed to learn from historical daily carbon emission patterns and then realize accurate real-time DCEM with real-time available generation-side measurements only. Case studies on the 118-bus power system operated with day-ahead planning considering carbon emission are performed to demonstrate the accuracy and effectiveness of the proposed method.