Abstract:Several challenges are encountered while integrating microgrids (MGs) with an existing utility grid. These are low inertia, intermittent nature of renewable energy resources (RESs), sensor and actuator faults, unbalanced and nonlinear loads, supply-demand mismatch, and uncertain switching functions of the power electronic converter. MG control is fully dependent on the communication network, which is also susceptible to different types of failures such as noise in communication links, communication delay, limited bandwidth, packet dropout, and various malicious cyber attacks. Very few papers are available in the literature that focus on the review of control schemes for MG. Consequently, this paper presents a comprehensive review of different robust and adaptive control schemes that address the challenges encountered due to communication constraints, uncertainties, and disturbances for different MG topologies such as AC, DC, and hybrid AC/DC MGs, as well as the current research trends in the field of robust and adaptive control. It can be concluded that to achieve control objectives of MG and overcome the existing challenges, robust and adaptive controllers show significantly improved performance in terms of transient and steady-state behavior and robustness as compared to traditional controllers.

Abstract:Heavy renewable penetrations and high-voltage cross-regional transmission systems reduce the inertia and critical frequency stability of power systems after disturbances. Therefore, the power system operators should ensure the frequency nadirs after possible disturbances are within the set restriction, e.g., 0.20 Hz. Traditional methods utilize linearized and simplified control models to quantify the frequency nadirs and achieve frequency-constrained unit commitments (FCUCs). However, the simplified models are hard to depict the frequency responses of practical units after disturbances. Also, they usually neglect the regulations from battery storage. This paper achieves FCUCs with linear rules extracted from massive simulation results. We simulate the frequency responses on typical thermal-hydro-storage systems under diverse unit online conditions. Then, we extract the rules of frequency nadirs after disturbances merely with linear support vector machine to evaluate the frequency stability of power systems. The algorithm holds a high accuracy in a wide range of frequency restrictions. Finally, we apply the rules to three typical cases to show the influences of frequency constraints on unit commitments.

Abstract:As the steady-state frequency of an actual power system decreases from its nominal value, the composite load of the system generally responds positively to lower power consumption, and vice versa. It is believed that this load frequency damping (LFD) effect will be artificially enhanced, i.e., sensitivities of loads with respect to operational frequency will increase, in future power systems. Thus, for wind-integrated power systems, this paper proposes a frequency-dependent chance constrained unit commitment (FDCCUC) model that employs the operational frequency as a dispatching variable so that the LFD effect-based load power can act as a supplemental reserve. Because the frequency deviation is safely restricted, this low-cost reserve can be sufficiently exerted to upgrade the wind power accommodation capability of a power system that is normally confined by an inadequate reserve to cope with uncertain wind power forecasting error. Moreover, when the FDCCUC model is applied to a bulk AC/DC hybrid power system consisting of several independently operated regional AC grids interconnected by DC tie-lines, a hierarchically implemented searching algorithm is proposed to protect private scheduling information of the regional AC grids. Simulations on a 2-area 6-bus system and a 3-area 354-bus system verify the effectiveness of the FDCCUC model and hierarchical searching algorithm.

Abstract:In this paper，a robust adaptive unscented Kalman filter (RAUKF) is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model. To address these issues, a robust M-estimator is first utilized to update the measurement noise covariance. Next, to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation, an adaptive update method is produced. The proposed method is integrated with spherical simplex unscented transformation technology, and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties. Finally, the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system. Compared with other methods, the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.

Abstract:Gaussian assumptions of non-Gaussian noises hinder the improvement of state estimation accuracy. In this paper, an asymmetric generalized Gaussian distribution (AGGD), as a unified representation of various unimodal distributions, is applied to formulate the non-Gaussian forecasting-aided state estimation problem. To address the problem, an improved particle filter is proposed, which integrates a near-optimal AGGD proposal function and an AGGD sampling method into the typical particle filter. The AGGD proposal function can approximate the target distribution of state variables to greatly alleviate particle degeneracy and promote precise estimation, through considering both state transitions and latest measurements. For rapid particle generation from the AGGD proposal function, an efficient inverse cumulative distribution function (CDF) sampling method is employed based on the derived approximation of inverse CDF of AGGD. Numerical simulations are carried out on a modified balanced IEEE 123-bus test system. The results validate that the proposed method outperforms other popular state estimation methods in terms of accuracy and robustness, whether in Gaussian, non-Gaussian, or abnormal measurement errors.

Abstract:Dynamic state estimation (DSE) accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time. This paper proposes a DSE approach for a doubly-fed induction generator (DFIG) with unknown inputs based on adaptive interpolation and cubature Kalman filter (AICKF-UI). DFIGs adopt different control strategies in normal and fault conditions; thus, the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases. Consequently, the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs, which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter. Furthermore, as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs, a large estimation error may occur or the DSE approach may diverge. To this end, in this paper, a local-truncation-error-guided adaptive interpolation approach is developed. Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can ① effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient; ② accurately track the dynamic states and unknown inputs of the DFIG; and ③ deal with various types of system operating conditions such as time-varying wind and different system faults.

Abstract:Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems. However, the volatility and intermittence of wind power pose uncertainties to traditional point prediction, resulting in an increased risk of power system operation. To represent the uncertainty of wind power, this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network (GNN) and an improved Bootstrap technique. Specifically, adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective. Then, the graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) are proposed to capture spatiotemporal features between nodes in the graph. To obtain high-quality prediction intervals (PIs), an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively. Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph, and the prediction results outperform popular baselines on two real-world datasets, which implies a high potential for practical applications in power systems.

Abstract:Wind power curve modeling is essential in the analysis and control of wind turbines (WTs), and data preprocessing is a critical step in accurate curve modeling. As traditional methods do not sufficiently consider WT models, this paper proposes a new data cleaning method for wind power curve modeling. In this method, a model-data hybrid-driven (MDHD) outlier detection method is constructed, and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model. Simultaneously, because the MDHD outlier detection method considers multiple types of operating data of WTs, anomaly detection results require further analysis. Accordingly, an expert system is developed in which a knowledgebase and an inference engine are designed based on the coupling relationships of different operating data. Finally, abnormal data are eliminated and the power curve modeling is completed. The proposed and traditional methods are compared in numerical cases, and the superiority of the proposed method is demonstrated.

Abstract:Experimental and theoretical studies have confirmed that, relative to a one-shot voltage fault, a doubly-fed induction generator (DFIG) will suffer a greater transient impact during continuous voltage faults. This paper presents the design and application of an effective scheme for DFIGs when a commutation failure (CF) occurs in a line-commutated converter based high-voltage direct current (LCC-HVDC) transmission system. First, transient demagnetization control without filters is proposed to offset the electromotive force (EMF) induced by the natural flux and other low-frequency flux components. Then, a rotor-side integrated impedance circuit is designed to limit the rotor overcurrent to ensure that the rotor-side converter (RSC) is controllable. Furthermore, coordinated control of the demagnetization and segmented reactive currents is implemented in the RSC. Comparative studies have shown that the proposed scheme can limit rotor fault currents and effectively improve the continuous fault ride-through capability of DFIGs.

Abstract:Anomaly detection based on the data collected from the supervisory control and data acquisition (SCADA) system is crucial to reduce the failure rate of wind turbines (WTs). The difficulty of this kind of methods is to dynamically identify the threshold for anomaly detection under changing operating conditions. In this paper, a generalized WT anomaly detection method based on the combined probability estimation model (CPEM) is proposed. The CPEM can estimate the conditional probability density function (PDF) of the target variable given changing conditions. Its generalization and accuracy are better than those of the independent probability estimation model because it combines the advantages of various kinds of probability estimation models through linear combination. By using the CPEM, the normal operating bounds under different operating conditions can be obtained, which dynamically form the thresholds for anomaly detection. Meanwhile, with respect to the thresholds, hypothesis testing (HT) is adopted to identify the anomaly by inspecting whether the observations exceed the thresholds at a given significance level, providing sound mathematical support for anomaly detection and making the detection results more reliable. The effectiveness of the proposed method is tested by using the actual data of WTs with known faults. The results illustrate that the proposed method can detect the abnormal operating state of the gearbox and generator much more early than the system fault alarm.

Abstract:The dynamic coupling effect, which is introduced by the dual-sequence phase-locked loops (PLLs) used in doubly- fed induction generator (DFIG) based wind energy generation systems (WEGSs) during asymmetric low voltage ride-through (LVRT) in weak grid, needs attention. In order to study this new dynamic coupling effect, an equivalent two-degree-of-freedom (2-DOF) spring damper particle model is used in this paper to develop a small-signal model for the dual-sequence PLLs. The dynamic interaction between the positive-sequence (PS) and negative-sequence (NS) PLLs is unveiled. Moreover, the impact of the dynamic coupling between the dual-sequence PLLs on the dynamic stability during the steady-state stage of an asymmetric fault is analyzed. The analysis results show that the dynamic coupling between the dual-sequence PLLs will cause drift in the frequency and damping for the PS and NS PLL modes. This will change the instability modal of the system and introduce the risk of dynamic instability. Hence, the effectiveness of existing control strategies for enhancing the dynamic stability will be decreased. Finally, a novel PLL structure is designed to improve the dynamic stability of the system during the steady-state stage of an asymmetric fault. The effectiveness of the proposed strategy is verified by simulations and experiments.

Abstract:This paper aims to improve the performance of the conventional perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. As the oscillation around the maximum power point (MPP) is the main disadvantage of this technique, we introduce a modified P&O algorithm to conquer this handicap. The new algorithm recognizes approaching the peak of the photovoltaic (PV) array power curve and prevents the oscillation around the MPP. The key to achieve this goal is testing the change of output power in each cycle and comparing it with the change in array terminal power of the previous cycle. If a decrease in array terminal power is observed after an increase in the previous cycle or in the opposite direction, an increase in array terminal power is observed after a decrease in the previous cycle; it means we are at the peak of the power curve, so the duty cycle of the boost converter should remain the same as the previous cycle. Besides, an optimized duty cycle is introduced, which is adjusted based on the operating point of PV array. Furthermore, a DC-DC boost converter powered by a PV array simulator is used to test the proposed concept. When the irradiance changes, the proposed algorithm produces an average ηMPPT of nearly 3.1% greater than that of the conventional P&O algorithm and the incremental conductance (InC) algorithm. In addition, under strong partial shading conditions and drift avoidance tests, the proposed algorithm produces an average ηMPPT of nearly 9% and 8% greater than that of the conventional algorithms, respectively.

Abstract:The essential task of integrated electricity-heat systems (IEHSs) is to provide customers with reliable electric and heating services. From the perspective of customers, it is reasonable to analyze the reliabilities of IEHSs based on the ability to provide energy services with a reasonable assurance of continuity and quality, which are termed as service-based reliabilities. Due to the thermal inertia existing in IEHSs, the heating service performances can present slow dynamic characteristics, which has a great impact on the service satisfaction of customers. The neglect of such thermal dynamics will bring about inaccurate service-based reliability measurement, which can lead to the inefficient dispatch decisions of system operators. Therefore, it is necessary to provide a tool which can analyze the service-based reliabilities of IEHSs considering the impacts of thermal dynamics. This paper firstly models the energy service performance of IEHSs in contingency states. Specifically, the nodal energy supplies are obtained from the optimal power and heat flow model under both variable hydraulic and thermal conditions, in which the transmission-side thermal dynamics are formulated. On this basis, the energy service performances for customers are further determined with the formulation of demand-side thermal dynamics. Moreover, a service-based reliability analysis framework for the IEHSs is proposed utilizing the time-sequential Monte Carlo simulation (TSMCS) technique with the embedded decomposition algorithm. Furthermore, the indices for quantifying service-based reliabilities are defined based on the traditional reliability indices, where dynamic service performances and service satisfactions of customers are both considered. Numerical simulations are carried out with a test system to validate the effectiveness of the proposed framework.

Abstract:When urban distribution systems are gradually modernized, the overhead lines are replaced by underground cables, whose shunt admittances can not be ignored. Traditional power flow (PF) model with π equivalent circuit shows non-convexity and long computing time, and most recently proposed linear PF models assume zero shunt elements. All of them are not suitable for fast calculation and optimization problems of modern distribution systems with non-negligible line shunts. Therefore, this paper proposes a linearized branch flow model considering line shunt (LBFS). The strength of LBFS lies in maintaining the linear structure and the convex nature after appropriately modeling the π equivalent circuit for network equipment like transformers. Simulation results show that the calculation accuracy in nodal voltage and branch current magnitudes is improved by considering shunt admittances. We show the application scope of LBFS by controlling the network voltages through a two-stage stochastic Volt/VAr control (VVC) problem with the uncertain active power output from renewable energy sources (RESs). Since LBFS results in a linear VVC program, the global solution is guaranteed. Case study exhibits that VVC framework can optimally dispatch the discrete control devices, viz. substation transformers and shunt capacitors, and also optimize the decision rules for real-time reactive power control of RES. Moreover, the computing efficiency is significantly improved compared with that of traditional VVC methods.

Abstract:This paper proposes a new distributed AC state estimation method. Different from the popular distributed state estimation (DSE) methods based on area partitioning method, the proposed method is a truly distributed method in which the power system is not required to be divided into smaller areas and a centralized state estimator in each area is not needed. In order to achieve fully DSE, the information propagation algorithm is introduced in this paper to help the distributed local state estimators share the measurement data. The information propagation algorithm is developed based on consensus protocol. The proof of the convergence of the information propagation algorithm is provided in this paper. Then, the AC state estimation method is integrated with the information propagation algorithm to realize the proposed method. The proposed method is tested in different standard power system models. The results show that the proposed method reaches the similar accuracy as the traditional centralized state estimation methods and performs faster and more accurate than the existing DSE methods.

Abstract:Bipolar direct current (DC) distribution networks can effectively improve the connection flexibility for renewable generations and loads. In practice, concerns regarding the potential voltage unbalance issue of the distribution networks and the frequency of switching still remain. This paper proposes a day-ahead polarity switching strategy to reduce voltage unbalance by optimally switching the polarity of renewable generations and loads while minimizing the switching times simultaneously in the range of a full day. First, a multi-objective optimization model is constructed to minimize the weighted sum of voltage unbalance factors and the sum of number of switching actions in the day based on the power flow model. Second, a two-step solution strategy is proposed to solve the optimization model. Finally, the proposed strategy is validated using 11-node and 34-node distribution networks as case studies, and a switching and stabilizing device is designed to enable unified switching of renewable generations and loads. Numerical results demonstrate that the proposed strategy can effectively reduce the switching times without affecting the improvement of voltage balance.

Abstract:Accurate information for consumer phase connectivity in a low-voltage distribution network (LVDN) is critical for the management of line losses and the quality of customer service. The wide application of smart meters provides the data basis for the phase identification of LVDN. However, the measurement errors, poor communication, and data distortion have significant impacts on the accuracy of phase identification. In order to solve this problem, this paper proposes a phase identification method of LVDN based on stepwise regression (SR) method. First, a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN. Second, the SR algorithm is used to identify the consumer phase connectivity. Third, by defining a significance correction factor, the results from the SR algorithm are updated to improve the accuracy of phase identification. Finally, an LVDN test system with 63 consumers is constructed based on the real load. The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.

Abstract:High-impedance faults (HIFs) in distribution networks may result in fires or electric shocks. However, considerable difficulties exist in HIF detection due to low-resolution measurements and the considerably weaker time-frequency characteristics. This paper presents a novel HIF detection method using synchronized current information. The method consists of two stages. In the first stage, joint key characteristics of the system are extracted with the minimal system prior knowledge to identify the global optimal micro-phase measurement unit (μPMU) placement. In the second stage, the HIF is detected through a multivariate Jensen-Shannon divergence similarity measurement using high-resolution time-synchronized data in μPMUs in a high-noise environment. l_2,1 principal component analysis (PCA), i.e., PCA based on the l_2,1 norm, is applied to an extracted system state and fault features derived from different resolution data in both stages. An economic observability index and HIF criteria are employed to evaluate the performance of placement method and to identify HIFs. Simulation results show that the method can reliably detect HIFs with reasonable detection accuracy in noisy environments.

Abstract:This paper introduces an AC stochastic optimal power flow (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An application of the AC SOPF is described where a distribution system operator (DSO) requires flexibility to each charging pool in a day-ahead time frame, minimizing the cost for flexibility while guaranteeing technical limits. Flexibility areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the solution. Results show that all players can benefit from this approach, i.e., the DSO obtains a risk-aware solution, while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

Abstract:In this paper, the synchronization stability challenges of same-rated frequency interconnected microgrids (IMGs) with fully inverter-based generation units are studied. In this type of weak power grid with low X/R ratios and low line impedances, no strong source with a high-inertia rating exists with which other generation units can be synchronized. Two IMGs controlled using a pinning consensus-based control architecture are considered. The inrush power flow at the beginning of the interconnection process is modeled and analyzed. This power flow is affected by the voltage/phase/frequency difference of the IMG points of common coupling. A small-signal model of the IMGs is obtained that includes a synchronization control unit, and small-signal stability is analyzed based on sensitivity analysis of the most important control and operational parameters. In addition, the transient stability of a nonlinear model of the IMGs under study as implemented in SimPowerSystems/MATLAB is investigated. Stable synchronization is more challenging than the synchronization of multi-area strong power grids and grid-connected MGs. However, synchronization can still be performed by selecting more limited ranges for the control gains and threshold values of the synchronization algorithm. Nevertheless, different disturbances such as high load conditions can cause synchronization instability.

Abstract:Stability in unbalanced power systems has deserved little attention in the literature. Given the importance of this scenario in distribution systems with distributed generation, this paper revisits modal analysis techniques for stability studies in power systems, and explains how to tackle unbalanced power systems with voltage-dependent loads. The procedure is described in detail and applied to a low-voltage (LV) simple case study with two grid-forming electronic power converters and unbalanced loads. Results are then compared with those obtained with the popular impedance-based method. While the latter is easier to implement using simulation or field data, the former requires complete information of the system, but gives a better insight into the problem. Since both methods are based on a small-signal approximation of the system, they provide similar results, but they discern different information. A larger second case study based on an LV CIGRE distribution system is also analysed. Results are obtained using a detailed Simulink model of the microgrids with electronic power converters.

Abstract:With the extensive penetration of distributed renewable energy and self-interested prosumers, the emerging power market tends to enable user autonomy by bottom-up control and distributed coordination. This paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer (P2P) energy sharing among prosumers. A novel cloud-edge-based We-Market is presented, where the prosumers, as edge nodes with independent control, balance the electricity cost and thermal comfort by formulating a dynamic household energy management system (HEMS). Meanwhile, the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility function. In the cloud center, a distributed convergence bidding (CB) algorithm based on consistency criterion is developed, which promotes faster and fairer bidding through the interactive iteration with the edge nodes. Besides, the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing capacity. Numerical results show the effectiveness and practicability of the proposed We-Market, which achieves 15% cost reduction with shorter running time. Comparative analysis indicates better scalability, which is more suitable for larger-scale We-Market implementation.

Abstract:At present, electrode line impedance supervision (ELIS) based protection is widely used to detect faults on grounding electrode lines, which are indispensable elements of high-voltage direct current (HVDC) systems. The existing theoretical analysis of measured impedance is based on lumped line model and the threshold value is generally set according to engineering experience, which have caused the dead zone problem and even accidents. Therefore, a study on measured impedance of ELIS-based protection and its threshold value selection method is carried out to solve this problem. In this study, the expressions of measured impedance under normal operation and fault conditions are deduced based on rigorous and accurate line model. Based on the expressions, the characteristics of the measured impedance are calculated and analyzed. With the characteristics of the measured impedance, the applicability of the protection with the traditional threshold value is further discussed and the distribution of the dead zone can be located. Then, the method to calculate the threshold value of ELIS-based protection is proposed. With a proper threshold value selected by the proposed method, the dead zone of ELIS-based protection is effectively eliminated, and the protection can identify all types of faults even with large transition resistances. Case studies on PSCAD/EMTDC have been conducted to verify the conclusion.

Abstract:In the voltage source converter based high-voltage direct current (VSC-HVDC) grids, fast and reliable protections are the key technologies. The traditional protection schemes are easily affected by fault resistance, line distributed capacitance, etc. Meanwhile, the influence of fault current limiting strategy (FCLS) has not been fully considered. In this paper, the fault characteristics under FCLS and the feasibility of traditional travelling wave protections are analyzed. To improve the reliability and sensibility, a similarity comparison based pilot protection scheme is proposed, which focuses on the relationship between the fault characteristics and the state of the protected transmission line, with the establishment of a precise frequency-dependent transmission line model. The criteria based on the similarity comparison calculated by cross-wavelet can identify the fault effectively. Meanwhile, the protection scheme can also endure the influence of error synchronization. Finally, the protection performance is verified in the PSCAD/EMTDC under different fault conditions.

Abstract:When a renewable energy station (RES) connects to the rectifier station (RS) of a modular multilevel converter-based high-voltage direct current (MMC-HVDC) system, the voltage at the point of common coupling (PCC) is determined by RS control methods. For example, RS control may become saturated under fault, and causes the RS to change from an equivalent voltage source to an equivalent current source, making fault analysis more complicated. In addition, the grid code of the fault ride-through (FRT) requires the RES to output current according to its terminal voltage. This changes the fault point voltage and leads to RES voltage regulation and current redistribution, resulting in fault response interactions. To address these issues, this study describes how an MMC-integrated system has five operation modes and three common characteristics under the duration of the fault. The study also reveals several instances of RS performance degradation such as AC voltage loop saturation, and shows that RS power reversal can be significantly improved. An enhanced AC FRT control method is proposed to achieve controllable PCC voltage and continuous power transmission by actively reducing the PCC voltage amplitude. The robustness of the method is theoretically proven under parameter variation and operation mode switching. Finally, the feasibility of the proposed method is verified through MATLAB/Simulink results.

Abstract:This paper performs a study on three-way subsynchronous torsional interactions (SSTI) between a hybrid dual-infeed high-voltage direct current (HVDC) system and a nuclear generator. The test case is based on the French IFA2000 line commutated converter (LCC) HVDC (2 GW) and the new Eleclink modular multilevel converter (MMC) HVDC (1 GW) interacting with the Gravelines generator (1 GW). The analysis is performed by the means of the eigenvalue stability assessment on an analytical model, while the accuracy of the conclusions is verified using the detailed non-linear electromegnetic transient program (EMTP) model. The study shows that the dual-infeed system may introduce higher risk of the SSTI compared with the point-to-point HVDC systems. It shows that MMC operating as static synchronous compensator (STATCOM) may further reduce the torsional damping at 6.3 Hz mode. This conclusion may be unexpected since it is known fact from literature that STATCOM has a beneficial impact on the transient performance of LCC. Further studies show that in a sequential HVDC loading, it may be beneficial to load the MMC HVDC first. Also, the risk of the SSTI may be minimized by changing HVDC controller gains, in particular, by increasing phase-locked-loop (PLL) gains on the LCC rectifier.

Abstract:Grid-forming (GFM) control based high-voltage DC (HVDC) systems and renewable energy sources (RESs) provide support for enhancing the stability of power systems. However, the interaction and coordination of frequency support between the GFM-based modular multilevel converter based HVDC (MMC-HVDC) and grid-following (GFL) based RESs or GFM-based RESs have not been fully investigated, which are examined in this study. First, the detailed AC- and DC-side impedances of GFM-based MMC-HVDC are analyzed. The impedance characteristics of GFL- and GFM-based wind turbines are next analyzed. Then, the influences of GFL- and GFM-based wind farms (WFs) on the DC- and AC-side stabilities of WF-integrated MMC-HVDC systems are compared and evaluated. The results show that the GFM-based wind turbine performs better than the GFL-based wind turbine. Accordingly, to support a receiving-end AC system, the corresponding frequency supporting strategies are proposed based on the GFM control for WF-integrated MMC-HVDC systems. The GFM-based WF outperforms the GFL-based WF in terms of stability and response time. Simulations in PSCAD/EMTDC demonstrate the DC- and AC-side stability issues and seamless grid support from the RESs, i.e., WFs, to the receiving-end AC system.

Abstract:Unpredictable power fluctuation and fault ride-through capability attract increased attention as two uncertain major factors in doubly-fed induction generators (DFIGs) integrated DC power system. Present solutions usually require complicated cooperation comprising multiple modules of energy storage, current control, and voltage stabilizer. To overcome the drawbacks of existing solutions, this paper proposes a superconducting magnetic energy storage (SMES) integrated current-source DC/DC converter (CSDC). It is mainly composed of a current-source back-to-back converter, and the SMES is tactfully embedded in series with the intermediate DC link. The proposed SMES-CSDC is installed in front of the DC-DFIG to carry out its dual abilities of load voltage stabilization under multifarious transient disturbances and power regulation under wind speed variations. Compared with the existing DC protection devices, the SMES-CSDC is designed on the basis of unique current-type energy storage. It has the advantages of fast response, extensive compensation range, concise hardware structure, and straightforward control strategy. The feasibility of the SMES-CSDC is implemented via a scaled-down experiment, and its effectiveness for DC-DFIG protection is verified by a large-scale DC power system simulation.

Abstract:Generalized short circuit ratio (gSCR) for grid strength assessment of multi-infeed high-voltage direct current (MIDC) systems is a rigorous theoretical extension of the traditional SCR, which enables SCR to be extended to MIDC systems. However, gSCR is originally based on the assumption of homogeneous MIDC systems, in which all high-voltage direct current (HVDC) converters have an identical control configuration, thus presenting challenges to applications of gSCR to inhomogeneous MIDC systems. To weaken this assumption, this paper applies matrix perturbation theory to explore the possibility of utilization of gSCR into inhomogeneous MIDC systems. Results of numerical experiments show that in inhomogeneous MIDC systems, the previously proposed gSCR can still be used without modification. However, critical gSCR (CgSCR) must be redefined by considering the characteristics of control configurations of HVDC converter. Accordingly, the difference between gSCR and redefined CgSCR can effectively quantify the pertinent AC grid strength in terms of the static-voltage stability margin. The performance of the proposed method is demonstrated in a triple-infeed inhomogeneous line commutated converter based high-voltage direct current (LCC-HVDC) system.