Abstract:Electric vehicles (EVs) are becoming more popular worldwide due to environmental concerns, fuel security, and price volatility. The performance of EVs relies on the energy stored in their batteries, which can be charged using either AC (slow) or DC (fast) chargers. Additionally, EVs can also be used as mobile power storage devices using vehicle-to-grid (V2G) technology. Power electronic converters (PECs) have a constructive role in EV applications, both in charging EVs and in V2G. Hence, this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications. It examines PECs from the point of view of their classifications, configurations, control approaches, and future research prospects and their impacts on power quality. These can be classified into various topologies: DC-DC converters, AC-DC converters, DC-AC converters, and AC-AC converters. To address the limitations of traditional DC-DC converters such as switching losses, size, and high-electromagnetic interference (EMI), resonant converters and multiport converters are being used in high-voltage EV applications. Additionally, power-train converters have been modified for high-efficiency and reliability in EV applications. This paper offers an overview of charging topologies, PECs, challenges with solutions, and future trends in the field of the EV charging station applications.

Abstract:Rotor angle stability (RAS) prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems. The wide deployment of phasor measurement units (PMUs) promotes the development of data-driven methods for RAS prediction. This paper proposes a temporal and topological embedding deep neural network (TTEDNN) model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data. The grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power grid. Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered. Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance, scalability, and robustness against measurement uncertainties of the TTEDNN model. Results show that the TTEDNN model performs best among existing deep learning models. Furthermore, the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

Abstract:This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units (PMUs) to assess and classify generating-side voltage trajectories. The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block. In addition, the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operational state. Different types of short-term voltage dynamics are considered in the proposed method. The fuzzy membership functions are determined using a systematic method that considers the current situation of the voltage trajectory. Finally, the voltage status is categorized into four classes to determine appropriate remedial actions. The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.

Abstract:Volt-var control (VVC) is essentially a non-convex optimization problem due to the non-convexity of power flow (PF) constraints, resulting in the difficulty in obtaining the optimum without convexity conversion. The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables, which in turn increases the optimization difficulty or even leads to optimization failure. This paper first proposes a deterministic VVC method based on convex deep learning power flow (DLPF). This method uses the input convex neural network (ICNN) to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment, which can ensure the global optimum with extremely fast computation speed. To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC, this paper proposes robust VVC method based on convex deep learning interval power flow (DLIPF), which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval. Combining DLIPF with DLPF, this method decreases the modeling and optimization difficulty of robust VVC significantly. Test results on 30-bus, 118-bus, and 200-bus systems prove the correctness and rapidity of the proposed methods.

Abstract:A day-ahead voltage-stability-constrained network topology optimization (DVNTO) problem is proposed to find the day-ahead topology schemes with the minimum number of operations (including line switching and bus-bar splitting) while ensuring the sufficient hourly voltage stability margin and the engineering operation requirement of power systems. The AC continuation power flow and the uncertainty from both renewable energy sources and loads are incorporated into the formulation. The proposed DVNTO problem is a stochastic, large-scale, nonlinear integer programming problem. To solve it tractably, a tailored three-stage solution methodology, including a scenario generation and reduction stage, a dynamic period partition stage, and a topology identification stage, is presented. First, to address the challenges posed by uncertainties, a novel problem-specified scenario reduction process is proposed to obtain the representative scenarios. Then, to obtain the minimum number of necessary operations to alter the network topologies for the next 24-hour horizon, a dynamic period partition strategy is presented to partition the hours into several periods according to the hourly voltage information based on the voltage stability problem. Finally, a topology identification stage is performed to identify the final network topology scheme. The effectiveness and robustness of the proposed three-stage solution methodology under different loading conditions and the effectiveness of the proposed partition strategy are evaluated on the IEEE 118-bus and 3120-bus power systems.

Abstract:In recent years, reinforcement learning (RL) has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods. It has gradually been applied in the fields such as economic dispatch of power systems due to its strong self-learning and self-optimizing capabilities. However, existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration, which poses a risk of issuing instructions that threaten the safe operation of power system. Therefore, we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow (SCOPF) based on expert knowledge and safety layer to determine active power dispatch strategy, voltage optimization scheme of the units, and charging/discharging dispatch of energy storage systems. The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy. Additionally, to avoid line overload, we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem. Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.

Abstract:The increasing penetration of renewable energy sources (RESs) brings great challenges to the frequency security of power systems. The traditional frequency-constrained unit commitment (FCUC) analyzes frequency by simplifying the average system frequency and ignoring numerous induction machines (IMs) in load, which may underestimate the risk and increase the operational cost. In this paper, we consider a multi-area frequency response (MAFR) model to capture the frequency dynamics in the unit scheduling problem, in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations. A multi-area FCUC (MFCUC) is formulated as mixed-integer nonlinear programming (MINLP) on the basis of the MAFR model. Then, we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently. The original MINLP is decomposed into a master problem and subproblems. The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution. Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs. Moreover, simulation results verify the ability of the proposed MAFR model to reflect regional frequency security and the available inertia of IMs in unit scheduling.

Abstract:With the large-scale integration of renewable energy, the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy generation. Simultaneously, in a market-oriented operation mode, the power dispatching control center aims to reduce the overall power purchase cost while ensuring the security of the power system. Therefore, a security-constrained transmission maintenance optimization model considering generation and operational risk costs is proposed herein. This model is built on double-layer optimization framework, where the upper-layer model is used for maintenance and generation planning, and the lower-layer model is primarily used to address the operational security risk arising from the random prediction error and N -1 transmission failure. Correspondingly, a generation-maintenance iterative algorithm based on a defined cost feedback is included to increase solution efficiency. Generation cost is determined using long-term security-constrained unit commitment, and the operational risk cost is obtained using a double-layer N-1 risk assessment model. An electrical correlation coupling coefficient is proposed for the solution process to avoid maintenance of associated equipment simultaneously, thereby improving model convergence efficiency. The IEEE 118-bus system is used as a test case for illustration, and test results suggest that the proposed model and algorithm can reduce the total cost of transmission maintenance and system operation while effectively improving the solution efficiency of the joint optimization model.

Abstract:As the proportion of converter-interfaced renewable energy resources in the power system is increasing, the strength of the power grid at the connection point of wind turbine generators (WTGs) is gradually weakening. Existing research has shown that when connected with the weak grid, the stability of the traditional grid-following controlled converters will deteriorate, and unstable phenomena such as oscillation are prone to arise. Due to the limitations of linear analysis that cannot sufficiently capture the stability phenomena, transient stability must be investigated. So far, standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability. However, the time-domain simulations have proven to be computationally too heavy, while analytical methods are difficult to formulate for larger systems, require many modelling assumptions, and are often conservative in estimating the stability boundary. This paper proposes and demonstrates an innovative approach to estimating the transient stability boundary via combining the linear Lyapunov function and the reverse-time trajectory technique. The proposed methodology eliminates the need of time-consuming simulations and the conservative nature of Lyapunov functions. This study brings out the clear distinction between the stability boundaries with different post-fault active current ramp rate controls. At the same time, it provides a new perspective on critical clearing time for wind turbine systems. The stability boundary is verified using time-domain simulation studies.

Abstract:With the increasing wind power penetration in the power system, the auxiliary frequency control (AFC) of wind farm (WF) has been widely used. The traditional system frequency response (SFR) model is not suitable for the wind power generation system due to its poor accuracy and applicability. In this paper, a piecewise reduced-order frequency response (P-ROFR) model is proposed, and an optimized auxiliary frequency control (O-AFC) scheme of WF based on the P-ROFR model is proposed. Firstly, a full-order frequency response model considering the change in operating point of wind turbine is established to improve the applicability. In order to simplify the full-order model, a P-ROFR model with second-order structure and high accuracy at each frequency response stage is proposed. Based on the proposed P-ROFR model, the relationship between the frequency response indexes and the auxiliary frequency controller coefficients is expressed explicitly. Then, an O-AFC scheme with the derived explicit expression as the optimization objective is proposed in order to improve the frequency support capability on the premise of ensuring the full release of the rotor kinetic energy and the full use of the effect of time delay on frequency regulation. Finally, the effectiveness of the proposed P-ROFR model and the performance of the proposed O-AFC scheme are verified by simulation studies.

Abstract:The operating conditions of wind turbines (WTs) in the same wind farm (WF) may share similarities due to their shared manufacturing process, control strategy, and operating environment. However, the similarities of WTs are seldom considered in WT anomaly detection, resulting in the disregard of useful information. This paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition (SCADA) data of multiple WTs in the same WF. First, a similarity assessment method based on a comparison of different observation time series is proposed, which objectively quantifies the similarities of WT operating conditions. Then, the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory (LSTM) algorithm. LSTM models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target WT. Finally, an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify anomalies. The effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

Abstract:This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic (PV) array, using external measurements of current and voltage at the inverter level. The estimated model is of interest to predict the power output of PV plants, in both planning and operation scenarios, and thus improves the efficient operation of power systems with high penetration of renewable energy. The proposed technique has been assessed in several simulated scenarios under different operating conditions. The results show that accurate estimates are provided for the model parameters, even in the presence of measurement noise and abrupt variations under the external conditions.

Abstract:In this paper, a novel multi-objective optimization model of integrated energy systems (IESs) is proposed based on the ladder-type carbon emission trading mechanism and refined load demand response strategies. First, the carbon emission trading mechanism is introduced into the optimal scheduling of IESs, and a ladder-type carbon emission cost calculation model based on rewards and penalties is established to strictly control the carbon emissions of the system. Then, according to different response characteristics of electric load and heating load, a refined load demand response model is built based on the price elasticity matrix and substitutability of energy supply mode. On these basis, a multi-objective optimization model of IESs is established, which aims to minimize the total operating cost and the renewable energy source (RES) curtailment. Finally, based on typical case studies, the simulation results show that the proposed model can effectively improve the economic benefits of IESs and the utilization efficiency of RESs.

Abstract:To improve the economic efficiency of urban integrated energy systems (UIESs) and mitigate day-ahead dispatch uncertainty, this paper presents an interconnected UIES and transmission system (TS) model based on distributed robust optimization. First, interconnections are established between a TS and multiple UIESs, as well as among different UIESs, each incorporating multiple energy forms. The Bregman alternating direction method with multipliers (BADMM) is then applied to multi-block problems, ensuring the privacy of each energy system operator (ESO). Second, robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty. The column and constraint generation (C&CG) algorithm is then employed to solve the robust model. Third, to tackle the convergence and practicability issues overlooked in the existing studies, an external C&CG with an internal BADMM and corresponding acceleration strategy is devised. Finally, numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.

Abstract:This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system (RIES). Our model regards the distribution network and each energy hub (EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic (PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers (ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.

Abstract:We propose an optimal stochastic scheduling strategy for a multi-vector energy complex (MEC), considering a full-blown model of the power-to-biomethane (PtM) process. Unlike conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane, a detailed PtM model is introduced to emphasize the reactor kinetics and chemical equilibria of methanation. This model crystallizes the interactions between the PtM process and MEC flexibility, allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic scenarios. Temperature optimization and flowsheet design of the PtM process increase the average selectivity of methane (i.e., ratio between net biomethane production and hydrogen consumption) up to 83.7% in the proposed synthesis flowsheet. Simulation results can provide information and predictions to operators about the optimal operating conditions of a PtM unit while improving the MEC flexibility.

Abstract:Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and reduce the inference time burden, this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs (topology and power loads) and reactive power scheduling schemes of distribution networks, from a data-driven perspective. The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix, a customized loss function, and the use of max-pooling layers. Additionally, a rule-based strategy is proposed to adjust infeasible solutions that violate constraints. Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions. Moreover, its online inference time is significantly faster than traditional physical model based methods, particularly for large-scale distribution networks.

Abstract:The increasing integration of intermittent renewable energy sources (RESs) poses great challenges to active distribution networks (ADNs), such as frequent voltage fluctuations. This paper proposes a novel ADN strategy based on multi-agent deep reinforcement learning (MADRL), which harnesses the regulating function of switch state transitions for the real-time voltage regulation and loss minimization. After deploying the calculated optimal switch topologies, the distribution network operator will dynamically adjust the distributed energy resources (DERs) to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm. Owing to the model-free characteristics and the generalization of deep reinforcement learning, the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments. Additionally, integrating parameter sharing (PS) and prioritized experience replay (PER) mechanisms substantially improves the strategic performance and scalability. This framework has been tested on modified IEEE 33-bus, IEEE 118-bus, and three-phase unbalanced 123-bus systems. The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

Abstract:DC microgrids (DCMGs) are made up of a network of sources and loads that are connected by a number of power electronic converters (PECs). The increase in the number of these PECs instigates major concerns in system stability. While interconnecting the microgrids to form a cluster, the system stability must be ensured. This paper proposes a novel step-by-step system matrix building (SMB) algorithm to update the system matrix of an existing DCMG cluster when a new microgrid is added to the cluster through a distribution line. The stability of the individual DCMGs and the DCMG cluster is analyzed using the eigenvalue method. Further, the particle swarm optimization (PSO) algorithm is used to retune the controller gains if the newly formed cluster is not stable. The simulation of the DCMG cluster is carried out in MATLAB/Simulink environment to test the proposed algorithm. The results are also validated using the OP4510 real-time simulator (RTS).

Abstract:In the realm of microgrid (MG), the distributed load frequency control (LFC) system has proven to be highly susceptible to the negative effects of false data injection attacks (FDIAs). Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG, this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system. Firstly, the method integrates a bi-directional long short-term memory (BiLSTM) neural network and an improved whale optimization algorithm (IWOA) into the LFC controller to detect and counteract FDIAs. Secondly, to enable the BiLSTM neural network to proficiently detect multiple types of FDIAs with utmost precision, the model employs a historical MG dataset comprising the frequency and power variances. Finally, the IWOA is utilized to optimize the proportional-integral-derivative (PID) controller parameters to counteract the negative impacts of FDIAs. The proposed detection and defense method is validated by building the distributed LFC system in Simulink.

Abstract:A reliable and robust communication network is essential to exchange information between distributed generators (DGs) and accurately calculate their control actions in microgrids (MGs). However, the integration of the communication network and MGs poses challenges related to the flexibility, availability, and reliability of the system. Furthermore, random communication disorders such as time delays and packet loss can negatively impact the system performance. Therefore, it is essential to design a suitable secondary controller (SC) with a fast dynamic response to restore voltage and appropriate power-sharing, while ensuring that the effects of random communication disorders are eliminated. In this regard, an optimal distributed hybrid model predictive secondary control method is presented in this paper. Realistic simulations are carried out in a mixed simulation environment based on MATLAB and OMNET++, by considering IEEE 802.11 (WiFi) using the recently developed Internet networking (INET) framework. In the implemented application layer, the recoveryUnit is responsible for reducing the impact of random communication disorders. The effectiveness and performance of the proposed method in comparison with a conventional model predictive control are verified by simulation results.

Abstract:This paper proposes an online framework to characterize demand response (DR) over time. The proposed framework facilitates obtaining and updating the daily consumption patterns of customers. The essential concept of response profile class (RPC) is introduced for characterization and complemented by the measure of the variability in customer behavior. This paper uses a modified version of the incremental clustering by fast search and find of density peaks (CFSFDP) algorithm for daily profiles, considering the multivariate normal kernel density estimator and incremental forms of the Davies-Bouldin (iDB) and Xie-Beni (iXB) validity indices. Case studies conducted using real-world and simulated daily profiles of residential and commercial Chilean end-users have demonstrated how the proposed framework can continuously characterize DR. The proposed framework is proven to achieve realistic customer models for effective energy management by estimating the customer response to price signals at the distribution system operator (DSO) level.

Abstract:Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet. Despite several studies on the mining of unique load characteristics, few studies have extensively considered the high computational burden and sample training. Based on low-frequency sampling data, a non-intrusive load monitoring algorithm utilizing the graph total variation (GTV) is proposed in this study. The algorithm can effectively depict the load state without the need for prior training. First, the combined K-means clustering algorithm and graph signals are used to build concise and accurate graph structures as load models. The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm. The introduction of the difference operator decreases the computing cost and addresses the inaccurate reconstruction of the graph signal. With low-frequency sampling data, the algorithm only requires a little prior data and no training, thereby reducing the computing cost. Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.

Abstract:Hybrid multi-terminal direct current (MTDC) transmission technology has been a research focus, and primary frequency regulation (FR) improvement in the receiving-end system is one of the problems to be solved. This paper presents a decentralized primary FR scheme for hybrid MTDC power systems considering multi-source enhancement to help suppress frequency disturbance in receiving-end systems. All the converters only need local frequency or DC voltage signal input to respond to system disturbance without communication or a control center, i.e., a decentralized control scheme. The proposed scheme can activate appropriate power sources to assist in FR in various system disturbance severities with fine-designed thresholds, ensuring sufficient utilization of each power source. To better balance FR performance and FR resource participation, an evaluation index is proposed and the parameter optimization problem is further conducted. Finally, the validity of the proposed scheme is verified by simulations in MATLAB/Simulink.

Abstract:The hybrid cascaded high-voltage direct current (HVDC) transmission system has various operation modes, and some operation modes are having sharply increasing requirements for protection rapidity, while the traditional pilot differential protection (PDP) has poor rapidity, and even refuses to operate when faults occur on the DC line. Therefore, a novel pilot protection scheme based on traveling wave characteristics is proposed. First, the adaptability of the traditional PDP applied in engineering is analyzed for different operation modes. Then, the expressions of the forward traveling wave (FTW) and backward traveling wave (BTW) on the rectifier side and the inverter side are derived for different fault locations. From the theoretical derivation, the difference between the BTW and FTW on the rectifier side is less than zero, and the same is true on the inverter side. However, in the event of an external fault of DC line, the difference between the BTW and FTW at near-fault terminal protection installation point is greater than zero. Therefore, by summing over the product of the difference between BTW and FTW of the rectifier side and that of the inverter side, the fault identification criterion is constructed. The simulation results show that the proposed pilot protection scheme can quickly and reliably identify the short-circuit faults of DC line in different operation modes.

Abstract:Two-level totem-pole power factor correction (PFC) converters in critical conduction mode (CRM) suffer from the wide regulation range of switching frequency. Besides, in high-frequency applications, the number of switching times increases, resulting in significant switching losses. To solve these issues, this paper proposes an improved modulation strategy for the single-phase three-level neutral-point-clamped (NPC) converter in CRM with PFC. By optimizing the discharging strategy and switching state sequence, the switching frequency and its variation range have been efficiently reduced. The detailed performance analysis is also presented regarding the switching frequency, the average switching times, and the effect of voltage gain. A 2 kW prototype is built to verify the effectiveness of the proposed modulation strategy and analysis results. Compared with the totem-pole PFC converter, the switching frequency regulation range of the three-level PFC converter is reduced by 36%, and the average switching times is reduced by 45%. The experimental result also shows a 1.2% higher efficiency for the three-level PFC converter in the full load range.

Abstract:Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty, a generic projection and decomposition (P&D) approach is proposed in this letter. The canonical min-max-min two-stage robust optimization (TSRO) model with coupling constraints is equivalent to a concise robust optimization (RO) model in the version of mixed-integer linear programming (MILP) via feasible region projection. The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm, which ensures the fast convergence to a high-quality solution in the distributed optimization. Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

Abstract:This letter studies large-disturbance stability of the power system with a synchronous generator (SG) and a converter-interfaced generation (CIG) connected to infinite bus. The power system is multi-timescale and first simplified. It is shown that the boundary of region of attraction (ROA) of the simplified model is composed of stable manifolds of unstable equilibrium point (UEP) or semi-singular point (SSP), named anchor points, and singular surface pieces. The type of anchor point determines the dominant instability pattern of the power system. When the anchor point is UEP or SSP, the dominant instability pattern is the instability of rotor angle of SG or the instability of phase-locked loop and outer control loop (OCL) of CIG, respectively. Transition of dominant instability pattern can be analyzed with the relative position relationship between UEP and SSP. The effect of OCL is discussed. When the OCL is activated, the ROA becomes smaller and the system is more prone to instability of CIG. It is necessary to consider the OCL when studying the large-disturbance stability of the power system.