Abstract:The ever-increasing dependence on electrical power has posed more challenges to power system engineers to deliver secure, stable, and sustained energy to electricity consumers. Due to the increasing occurrence of short- and long-term power interruptions in the power system, the need for a systematic approach to mitigate the negative impacts of such events is further manifested. Self-healing and its control strategies are generally accepted as a solution for this concern. Due to the importance of self-healing subject in power distribution systems, this paper conducts a comprehensive literature review on self-healing from existing published papers. The concept of self-healing is briefly described, and the published papers in this area are categorized based on key factors such as self-healing optimization goals, available control actions, and solution methods. Some proficient techniques adopted for self-healing improvements are also classified to have a better comparison and selection of methods for new investigators. Moreover, future research directions that need to be explored to improve self-healing operations in modern power distribution systems are investigated and described at the end of this paper.

Abstract:This paper proposes a probabilistic energy and reserve co-dispatch (PERD) model to address the strong uncertainties in high-renewable power systems. The expected costs of potential renewable energy curtailment/load shedding are fully considered in this model, which avoids insufficient or excessive emergency control capacity to produce more economical reserve decisions than conventional chance-constrained dispatch methods. Furthermore, an analytical reformulation approach of PERD is proposed to make it tractable. We firstly develop an approximation technique with high precision to convert the integral terms in objective functions into analytical ones. Then, the calculation of probabilistic constraints is equivalently transformed into an unconstrained optimization problem by introducing value-at-risk (VaR) representation. Specifically, the VaR formulas can be computed by a computationally-cheap dichotomy search algorithm. Finally, the PERD model is transformed into a convex problem, which can be solved reliably and efficiently using off-the-shelf solvers. Case studies are performed on IEEE test systems and real provincial power grids in China to illustrate the scalability and efficiency of the proposed method.

Abstract:The sudden generation-consumption imbalance is becoming more frequent in modern power systems, causing voltage and frequency stability issues. One potential solution is load participation in primary control. We formulate a novel optimal load control (NOLC) problem that aims to minimize the disutility of controllable loads in providing primary regulation. In this paper, we show that the network dynamics, coupled with well-defined load control (obtained via optimality condition), can be seen as an optimization algorithm to solve the dual problem of NOLC. Unlike most existing load control frameworks that only consider frequency response, our load-side primary control focuses on frequency, voltage, and aggregate cost. Simulation results imply that the NOLC approach can ensure better frequency and voltage regulations. Moreover, the coordination between NOLC and other devices enabled in the system, the NOLC performance against the total size of controllable loads, and the NOLC effectiveness in a multi-machine power system are also verified in MATLAB/Simulink.

Abstract:The deployment of dynamic reactive power source can effectively improve the voltage performance after a disturbance for a power system with increasing wind power penetration level and ubiquitous induction loads. To improve the voltage stability of the power system, this paper proposes an adaptive many-objective robust optimization model to deal with the deployment issue of dynamic reactive power sources. Firstly, two metrics are adopted to assess the voltage stability of the system at two different stages, and one metric is proposed to assess the tie-line reactive power flow. Then, a robustness index is developed to assess the sensitivity of a solution when subjected to operational uncertainties, using the estimation of acceptable sensitivity region (ASR) and D-vine Copula. Five objectives are optimized simultaneously ① total equipment investment; ② adaptive short-term voltage stability evaluation; ③ tie-line power flow evaluation; ④ prioritized steady-state voltage stability evaluation; and ⑤ robustness evaluation. Finally, an angle-based adaptive many-objective evolutionary algorithm (MaOEA) is developed with two improvements designed for the application in a practical engineering problem: ① adaptive mutation rate; and ② elimination procedure without a requirement for a threshold value. The proposed model is verified on a modified Nordic 74-bus system and a real-world power system. Numerical results demonstrate the effectiveness and efficiency of the proposed model.

Abstract:This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations (ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient (DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

Abstract:Electrical system planning of the large-scale offshore wind farm is usually based on N -1 security for equipment lectotype. However, in this method, owing to the aggregation effect in large-scale offshore wind farms, offshore electrical equipment operates under low load for long periods, thus wasting resources. In this paper, we propose a method for electrical system planning of the large-scale offshore wind farm based on the N+ design. A planning model based on the power-limited operation of wind turbines under the N+ design is constructed, and a solution is derived with the optimization of the upper power limits of wind turbines. A comprehensive evaluation and game analysis of the economy, risk of wind abandonment, and environmental sustainability of the planned offshore electrical systems have been conducted. Moreover, the planning of an in-field collector system, substation, and transmission system of an offshore electrical system based on the N+ design is integrated. For a domestic offshore wind farm, evaluation results show that the proposed planning method can improve the efficiency of wind energy utilization while greatly reducing the investment cost of the electrical system.

Abstract:The significance of situation awareness (SA) in power systems has increased to enhance the utilization of grid-connected renewable energy power generation (REPG). This paper proposes a real-time calculation architecture based on the integration of robust optimization (RO) and artificial intelligence. First, the time-series simulation of the REPG consumption capacity is carried out under the current grid operating conditions. RO is employed in this simulation, given the randomness of the REPG output and the grid load. Then, the radial basis function neural network (RBFNN) is trained with the results under different parameters using the artificial fish swarm algorithm (AFSA), enabling the neural network (NN) to be the replacement for the time-series simulation model. The trained NN can quickly perceive the REPG absorption situation within the predefined grid structure and period. Moreover, the Sobol’ method is adopted to conduct the global sensitivity analysis for different parameters based on the input-output samples obtained by the trained NN. Finally, the simulation experiments based on the modified IEEE 14-bus system prove the real-time performance and accuracy of the proposed SA architecture.

Abstract:Harmonic amplification phenomena could appear at the point of common connection (PCC) of the cable line terminal. However, the distributed parameter model of the cable line contains hyperbolic functions with plural variables, which makes it challenging to obtain the harmonic amplification factor (HAF). Hence, a time-domain method combining the Kalman filter and convolution inversion (KFCI) methods is proposed to address this problem. First, the Kalman filter method optimizes the square wave pulse response (SWPR) with measurement error. Then, the optimized SWPR data are used to get the HAF by the convolution inversion method. Next, the harmonic amplification characteristics of cable lines are explored. Finally, an experimental simulation model is built on the PSCAD software, verifying the optimization effectiveness of Kalman filter for the SWPR with error and the accuracy of the HAF calculated by the proposed method. The analysis rationality of harmonic amplification properties is also demonstrated.

Abstract:In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users’ privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection (CP) and linear programming (LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

Abstract:The optimal dispatch methods of integrated energy systems (IESs) currently struggle to address the uncertainties resulting from renewable energy generation and energy demand. Moreover, the increasing intensity of the greenhouse effect renders the reduction of IES carbon emissions a priority. To address these issues, a deep reinforcement learning (DRL)-based method is proposed to optimize the low-carbon economic dispatch model of an electricity-heat-gas IES. In the DRL framework, the optimal dispatch model of the IES is formulated as a Markov decision process (MDP). A reward function based on the reward-penalty ladder-type carbon trading mechanism (RPLT-CTM) is introduced to enable the DRL agents to learn more effective dispatch strategies. Moreover, a distributed proximal policy optimization (DPPO) algorithm, which is a novel policy-based DRL algorithm, is employed to train the DRL agents. The multithreaded architecture enhances the exploration ability of the DRL agents in complex environments. Experimental results illustrate that the proposed DPPO-based IES dispatch method can mitigate carbon emissions and reduce the total economic cost. The RPLT-CTM-based reward function outperforms the CTM-based methods, providing a 4.42% and 6.41% decrease in operating cost and carbon emission, respectively. Furthermore, the superiority and computational efficiency of DPPO compared with other DRL-based methods are demonstrated by a decrease of more than 1.53% and 3.23% in the operating cost and carbon emissions of the IES, respectively.

Abstract:The penetration of distributed energy resources (DERs) and energy-intensive resources is gradually increasing in active distribution networks (ADNs), which leads to frequent and severe voltage violation problems. As a densely distributed flexible resource in the future distribution network, 5G base station (BS) backup battery is used to regulate the voltage profile of ADN in this paper. First, the dispatchable potential of 5G BS backup batteries is analyzed. Considering the spatial-temporal characteristics of electric load for 5G BS, the dispatchable capacity of backup batteries at different time intervals is evaluated based on historical heat map data. Then, a voltage profile optimization model for ADN is established, consisting of 5G BS backup batteries and other voltage regulation resources. In this model, the charging/discharging behavior of backup batteries is based on its evaluation result of dispatchable capacity. Finally, the range of charging/discharging cost coefficients of 5G BS that benefits ADN and 5G operators are analyzed respectively. Further, an incentive policy for 5G operators is proposed. Under this policy, the charging/discharging cost coefficients of 5G BS can achieve a win-win situation for ADN and 5G operators. As an emerging flexible resource in ADN, the effectiveness and economy of 5G BS backup batteries participating in voltage profile optimization are verified in a test distribution network.

Abstract:With the increasing use of renewable resources and electric vehicles (EVs), the variability and uncertainty in their nature put forward a high requirement for flexibility in AC distribution system incorporating voltage source converter (VSC) based multi-terminal direct current (MTDC) grids. In order to improve the capability of distribution systems to cope with uncertainty, the flexibility enhancement of AC-MTDC distribution systems considering aggregated EVs is studied. Firstly, the charging and discharging model of one EV is proposed considering the users’ demand difference and traveling needs. Based on this, a vehicle-to-grid (V2G) control strategy for aggregated EVs to participate in the flexibility promotion of distribution systems is provided. After that, an optimal flexible dispatching method is proposed to improve the flexibility of power systems through cooperation of VSCs, controllable distributed generations (CDGs), aggregated EVs, and energy storage systems (ESSs). Finally, a case study of an AC-MTDC distribution system is carried out. Simulation results show that the proposed dispatching method is capable of effectively enhancing the system flexibility, reducing renewable power curtailment, decreasing load abandonment, and cutting down system cost.

Abstract:Weather-related failures significantly challenge the reliability of distribution systems. To enhance the risk management of weather-related failures, an interpretable extra-trees based weather-related risk prediction model is proposed in this study. In the proposed model, the interpretability is successfully introduced to extra-trees by analyzing and processing the paths of decision trees in extra-trees. As a result, the interpretability of the proposed model is reflected in the following three respects: it can output the importance, contribution, and threshold of weather variables at high risk. The importance of weather variables can help in developing a long-term risk prevention plan. The contribution of weather variables provides targeted operation and maintenance advice for the next prediction period. The threshold of weather variables at high risk is critical in further preventing high risks. Compared with the black-box machine learning risk prediction models, the proposed model overcomes the application limitations. In addition to generating predicted risk levels, it can also provide more guidance information for the risk management of weather-related failures.

Abstract:This paper presents a properly designed branch current based state estimator (BCBSE) used as the main core of an accurate fault location approach (FLA) devoted to distribution networks. Contrary to the approaches available in the literature, it uses only a limited set of conventional measurements obtained from smart meters to accurately locate faults at buses or branches without requiring measurements provided by phasor measurement units (PMUs). This is possible due to the methods used to model the angular reference and the faulted bus, in addition to the proper choice of the weights in the state estimator (SE). The proposed approach is based on a searching procedure composed of up to three stages ① the identification of the faulted zones; ② the identification of the bus closest to the fault; and ③ the location of the fault itself, searching on branches connected to the bus closest to the fault. Furthermore, this paper presents a comprehensive assessment of the proposed approach, even considering the presence of distributed generation, and a sensitivity study on the proper weights required by the SE for fault location purposes, which can not be found in the literature. Results show that the proposed BCBSE-based FLA is robust, accurate, and aligned with the requirements of the traditional and active distribution networks.

Abstract:With the booming of electric vehicles (EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the charging behaviors of household EVs are concentrated on low-cost periods, thus generating new load peaks and affecting the secure operation of the medium- and low-voltage grids. This problem is particularly acute in many old communities with relatively poor electricity infrastructure. In this paper, a novel two-stage charging scheduling scheme based on deep reinforcement learning is proposed to improve the power quality and achieve optimal charging scheduling of household EVs simultaneously in active distribution network (ADN) during valley period. In the first stage, the optimal charging profiles of charging stations are determined by solving the optimal power flow with the objective of eliminating peak-valley load differences. In the second stage, an intelligent agent based on proximal policy optimization algorithm is developed to dispatch the household EVs sequentially within the low-cost period considering their discrete nature of arrival. Through powerful approximation of neural network, the challenge of imperfect knowledge is tackled effectively during the charging scheduling process. Finally, numerical results demonstrate that the proposed scheme exhibits great improvement in relieving peak-valley differences as well as improving voltage quality in the ADN.

Abstract:In the compression of massive compound power quality disturbance (PQD) signals in active distribution networks, the compression ratio (CR) and reconstruction error (RE) act as a pair of contradictory indicators, and traditional compression algorithms have difficulties in simultaneously satisfying a high CR and low RE. To improve the CR and reduce the RE, a hybrid compression method that combines a strong tracking Kalman filter (STKF), sparse decomposition, Huffman coding, and run-length coding is proposed in this study. This study first uses a sparse decomposition algorithm based on a joint dictionary to separate the transient component (TC) and the steady-state component (SSC) in the PQD. The TC is then compressed by wavelet analysis and by Huffman and run-length coding algorithms. For the SSC, values that are greater than the threshold are reserved, and the compression is finally completed. In addition, the threshold of the wavelet depends on the fading factor of the STKF to obtain a high CR. Experimental results of real-life signals measured by fault recorders in a dynamic simulation laboratory show that the CR of the proposed method reaches as high as 50 and the RE is approximately 1.6%, which are better than those of competing methods. These results demonstrate the immunity of the proposed method to the interference of Gaussian noise and sampling frequency.

Abstract:Reliable planning and operation of power distribution systems are of great significance. In this paper, the impact-increment based state enumeration (IIBSE) method is modified to adapt to the features of distribution systems. With the proposed method, the expectation, probabilistic, and duration reliability indices can be accurately obtained with a lower enumerated order of contingency states. In addition, the time-consuming optimal power flow (OPF) calculation can be replaced by a simple matrix operation for both independent and radial series failure states. Therefore, the accuracy and efficiency of the assessment process are improved comprehensively. The case of RBTS bus 6 system and IEEE 123 node test feeder system are utilized to test the performance of the modified IIBSE. The results show the superiority of the proposed method over Monte Carlo (MC) sampling and state enumeration (SE) methods in distribution systems.

Abstract:Energy storage devices can effectively balance the uncertain load and significantly reduce electricity costs in the community microgrids (C-MGs) integrated with renewable energy sources. Scheduling of energy storage is a multi-stage decision problem in which the decisions must be guaranteed to be nonanticipative and multi-stage robust (all-scenario-feasible). To satisfy these two requirements, this paper proposes a method based on a necessary and sufficient feasibility condition of scheduling decisions under the polyhedral uncertainty set. Unlike the most popular affine decision rule (ADR) based multi-stage robust optimization (MSRO) method, the method proposed in this paper does not require the affine decision assumption, and the feasible regions (the set of all feasible solutions) are not reduced, nor is the solution quality affected. A simple illustrative example and real-scale scheduling cases demonstrate that the proposed method can find feasible solutions when the ADR-based MSRO fails, and that it finds better solutions when both methods succeed. Comprehensive case studies for a real system are performed and the results validate the effectiveness and efficiency of the proposed method.

Abstract:A second-order compensation link is adopted to control voltage-controlled inverters (VCIs) in microgrid systems to enhance the performance of the power synchronization process of the inverter. The second-order compensation link is classified as both a real pole compensator (RPC) and a complex pole compensator (CPC) according to the pole position. Given a model for the VCI power output, the design process for the second-order compensation link, which is equipped with an RPC and a CPC, is detailed. Moreover, the frequency-domain compensation effects of the RPC and CPC are analyzed using the root locus and Bode diagrams of the system before and after compensation. Finally, the compensation effects of the two types of second-order compensators are compared with the commonly used high-pass filter using MATLAB/Simulink, which verifies the RPC and CPC strategies. Simulation results show that the two types of compensators designed in this study can effectively increase the system cutting frequency and improve the phase margin in the frequency domain while accelerating the power synchronization process, simultaneously making it smoother and reducing overshoot in the time domain. The RPC has better gain robustness, whereas the CPC has better time constant robustness. By implementing an RPC or a CPC, the dynamic time of the power synchronization compensation strategy is reduced within 0.5 s, and the overshoot is reduced within 10% in the experiments with two inverters.

Abstract:With the integration of distributed generation (DG) into a microgrid, fault detection has become a major task to accomplish. A scheme for microgrid feeder protection based on a newly proposed feature, ΔRP, defined as the ratio of the sum of positive-sequence real power (PSRP) at the two ends of the feeder to the larger of the PSRPs among the two ends, is proposed. If ΔRP is larger than a threshold, an internal fault is detected; otherwise, the fault is external or there is no fault. The proposed scheme is tested in various scenarios, including fault type, fault resistance, fault location, fault inception angle, varying DG penetration levels, and simultaneous, evolving, and composite faults. In addition to this, the proposed scheme offers a robust performance when subjected to noise, synchronization error, changes in sampling frequency, and changes in the topology of a microgrid. The dominancy of the proposed scheme is proven by a comprehensive comparative study with various available recent schemes. Test results on the IEEE 13-bus network indicate the viability of the proposed protection scheme for a microgrid. Finally, the proposed scheme has been validated on a real-time simulator.

Abstract:This paper proposes a distribution locational marginal pricing (DLMP) based bi-level Stackelberg game framework between the internet service company (ISC) and distribution system operator (DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads (IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

Abstract:Multi-energy integrations provide great opportunities for economic and efficient resource utilization. In the meantime, power system operation requires enough flexible resources to deal with contingencies such as transmission line tripping. Besides economic benefits, this paper focuses on the security benefits that can be provided by multi-energy integrations. This paper first proposes an operation scheme to coordinate multiple energy production and local system consumption considering transmission networks. The integrated flexibility model, constructed by the feasible region of integrated demand response (IDR), is then formulated to aggregate and describe local flexibility. Combined with system security constraints, a multi-energy system operation model is formulated to schedule multiple energy production, transmission, and consumption. The effects of local system flexibility on alleviating power flow violations during N-1 line tripping contingencies are then analyzed through a multi-energy system case. The results show that local system flexibility can not only reduce the system operation costs, but also reduce the probability of power flow congestion or violations by approximately 68.8% during N-1 line tripping contingencies.

Abstract:Due to their fast response and strong short-term power throughput capacity, electric vehicles (EVs) are promising for providing primary frequency support to power grids. However, due to the complicated charging demands of drivers, it is challenging to efficiently utilize the regulation capacity of EV clusters for providing stable primary frequency support to the power grid. Accordingly, this paper proposes an adaptive primary frequency support strategy for EV clusters constrained by the charging-behavior-defined operation area. First, the forced charging boundary of the EV is determined according to the driver’s charging behavior, and based on this, the operation area is defined. This ensures full utilization of the available frequency support capacity of the EV. An adaptive primary frequency support strategy of EV clusters is then proposed. The output power of EV is adaptively regulated according to the real-time distance from the EV operating point to the forced charging boundary. With the proposed strategy, when the EV approaches the forced charging boundary, its output power is gradually reduced to zero. Then, the rapid state-of-charge declines of EVs and sudden output power reductions in EV clusters caused by forced charging to meet the driver’s charging demands can be effectively avoided. EV clusters can then provide sustainable frequency support to the power grid without violating the driver’s charging demands. Simulation results validate the proposed operation-area-constrained adaptive primary frequency support strategy, which outperforms the average strategy in terms of stable output maintenance and the optimal utilization of regulation capacities of EV clusters.

Abstract:Earlier studies have reported some calculation methods for commutation failure fault level (CFFL) in line-commutated-converter based high-voltage direct current (LCC-HVDC) system under single-line-to-ground (SLG) faults. The accuracy of earlier methods is limited because they only consider the commutating voltage drop and phase shift, while neglecting the DC current variation. Hence, this paper proposes a CFFL calculation method under SLG faults considering DC current variation, for better planning and designing of LCC-HVDC systems. First, the fault commutating voltage magnitude and phase shift are calculated. Then, the fault DC voltage during different commutation processes is deduced. Based on the commutating voltage magnitude and phase shift, and DC voltage during different commutation processes under SLG faults, the characteristics of CFFL with different fault time are demonstrated and analyzed. Next, the transient time-domain response of the DC current after the fault is obtained based on the DC transmission line model. Discrete commutation processes are constructed based on the commutation voltage-time area rule to solve the extinction angle under different fault levels and fault time. Finally, the CFFL is calculated considering the fault time, commutating voltage drop, phase shift, and DC current variation. The accuracy of the proposed method compared with the traditional method is validated based on the CIGRE benchmark model in PSCAD/EMTDC.

Abstract:This paper proposes a joint limiting control strategy for suppressing DC fault current and arm current in modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems, which includes two target-oriented current limiting controls. To limit the DC fault current in the early fault stage, an equivalent modular multilevel converter (MMC) impedance is obtained, and its high-frequency part is reshaped by introducing virtual impedance, which is realized by adjusting the inserted submodules adaptively. Following the analysis of MMC control characteristics, the arm current limiting strategy is investigated, with results showing that the inner-loop control has significant effects on arm current and that a simple low-pass filter can reduce the arm current in the fault period. Finally, by combining the virtual impedance shaping and inner-loop control, the fault currents of DC lines and MMC arms can be suppressed simultaneously, which can not only alleviate the interrupting pressure of the DC circuit breaker, but also prevent the MMC from being blocked by the arm overcurrent. Theoretical analysis conclusions and the proposed strategy are verified offline by a digital time-domain simulation on Power Systems Computer Aided Design/Electromagnetic Transients including DC platform, and experiment on a real-time digital simulator platform.

Abstract:A three-phase to single-phase modular multilevel converter based advanced co-phase traction power supply (MMC-ACTPS) system is an effective structure to address the concerns of phase splitting and poor power quality of the conventional electrified railway. Due to the large number of MMC-ACTPS system modules, I/O resources and computing speed have high requirements on processors. Moreover, the module capacitor balance is challenging because the sorting time is too long when the traditional sorting algorithm for voltage balance is used. To solve the above issues, a digital implementation scheme of flexible power control strategy for three-phase to single-phase MMC-ACTPS system based on field programmable gate array (FPGA), which has sufficient I/O resources, has been proposed. Due to the parallel execution characteristics of the FPGA, the execution time of the controller and the modulator can be greatly reduced compared with a digital signal processor (DSP) + FPGA or DSpace. In addition, an improved sorting algorithm is proposed to reduce the sorting time and the implementation steps are analyzed. Finally, simulation and experimental results are presented to demonstrate the effectiveness and correctness of the proposed control strategy.

Abstract:The modular multilevel converters (MMCs) are popularly used in high-voltage direct current (HVDC) transmission systems. However, for the direct modulation based MMC, its complex internal dynamics and the interaction with the grid impedance would induce the frequency coupling effect, which may lead to instability issues， especially in the case of weak grid. To effectively suppress the sub- and super-synchronous oscillations, this paper proposes a linear active disturbance rejection control (LADRC) based MMC control strategy. The LADRC mainly consists of the linear extended state observer (LESO) and the linear state error feedback (LSEF). And it is a potential method to enhance the system stability margin, attributing to its high anti-interference capability and good tracking performance. Thereupon, the system small-signal impedance model considering frequency coupling is established. And the effect of the introduction of the LADRC on the system stability is further investigated using the Nyquist criterion. Particularly, the influences of key control parameters on the stability are discussed in detail. Meanwhile, the impact of LADRC on the transient performance is explored through closed-loop zero poles. Finally, the correctness of the theoretical analysis and the effectiveness of the proposed control strategy are verified via electromagnetic simulations.

Abstract:This letter develops a fast analytical method for uncertainty quantification of electromechanical oscillation frequency due to varying generator dampings. By employing the techniques of matrix determinant reduction, two types of uncertainty analysis are investigated to quantify the impact of the generator damping on electromechanical oscillation frequency, i.e., interval analysis and probabilistic analysis. The proposed analytical frequency estimation formula is verified against conventional methods on two transmission system models. Then, Monte Carlo experiments and interval analysis are respectively conducted to verify the established lower/upper bound formulae and probability distribution formulae. Results demonstrate the accuracy and speed of the proposed method.