Lili He

This article proposes a novel N–1 interval optimal power flow (NIOPF) scheme that considers N–1 contingencies and interval uncertainties in renewable generation. Based on the scenario-equivalent decoupling method (SEDM), the NIOPF addresses the problems associated with existing methods, including constraint violations and low solution efficiency. First, the NIOPF model is formulated by integrating security constraints involving interval variables under both N–0 normal and N–1 contingency scenarios, along with scenario transition constraints. Second, a credible domain reduction method (CDRM) based on the Minkowski difference is proposed to handle interval constraints, converting them into tractable deterministic constraints that guarantee the system operates within safe limits across all uncertain scenarios. Then, the SEDM represents numerous N–1 scenarios equivalently as two positive and negative deviation scenarios, effectively reducing the dimensionality of N–1 scenarios. Next, a two-stage optimization model that integrates the N–0 normal operating scenario with the two equivalent deviation scenarios is constructed and solved iteratively to improve computational efficiency. Finally, case studies on the modified IEEE 30-bus system verify that the proposed SEDM ensures constraint satisfaction under uncertainties while improving computational efficiency. Further tests on the modified IEEE 118-bus and PEGASE 1354-bus systems confirm its engineering applicability.

Existing fault location approaches necessitate the deployment of micro-phasor measurement units ( $\boldsymbol {\mu }$ PMUs) at all terminal buses to achieve precise fault location on any line, leading to high costs and limited applicability in large, unbalanced active distribution networks (ADNs) with numerous branches. To address this issue, this paper proposes a cost-effective fault location method based on a sparse hybrid measurement scheme (SHMS), which replaces $\boldsymbol {\mu }$ PMUs on branches with conventional measuring devices (CMDs). First, a sparse fault current extraction method is proposed, and the conditions for the uniqueness of the sparse fault current solution are provided. Building on these results, a low-cost SHMS is proposed. Finally, a two-stage fault location method is presented to precisely locate faults on any line within ADNs. For the unbalanced IEEE 123-bus test system with numerous branches, the proposed method requires only $13~\boldsymbol {\mu }$ PMUs and 32 CMDs—compared to the conventional requirement of $45~\boldsymbol {\mu }$ PMUs—to locate faults on any line, thereby significantly reducing measurement costs. Simulation results indicate that, under diverse fault conditions, the fault location success rate reaches 98.30% for three-phase main lines and 96.16% for branches.

This paper proposes an optimal power flow method considering interval uncertainties (OPFIU) to enhance the security and economy of power systems with high renewable energy penetration. The OPFIU incorporates bi-level interval power flow (BIPF) equations, security and operation constraints, and active power balance across two extreme renewable output scenarios. Firstly, a dynamic-balance optimization scenario method (DOSM) is proposed to solve the BIPF within the OPFIU. The BIPF involves multiple generators in dynamically balancing the system’s active power, thus reducing the conservatism prevalent in existing interval power flow methods. Next, based on the BIPF results, an adaptive security limits definition method (ASLDM) is presented to convert the OPFIU model into a deterministic optimal power flow model, making it more tractable for solution. The ASLDM not only improves the solution efficiency and convergence of existing interval optimization methods, but also provides enhanced reliability in maintaining transmission power within operational limits across a broad spectrum of uncertainty scenarios. Moreover, the ASLDM, unlike robust optimization methods that require convexification of nonlinear AC power flow equations, partially avoids potential issues such as accuracy degradation or solution infeasibility resulting from model simplifications. The Simulation results, compared with robust optimization and chance-constrained programming methods, demonstrate that the proposed ASLDM improves both solution efficiency and convergence in solving the OPFIU problem.

More and more distribution generators (DGs) are integrated into renewable energy systems, which increases the transient instability risk of systems. The Takagi-Sugeno (TS) fuzzy multimodeling method is usually applied in transient stability analysis of power electronic systems. However, the traditional TS fuzzy multimodeling method is based on the linear matrix inequalities (LMIs) calculations and attraction domains search with the fixed search directions and step size, which severely increases conservation and calculation burden while evaluating the stability of high-order systems. To fill this gap, a large signal stability analysis method based on attraction domains directionless search is proposed in this paper. Firstly, the drawbacks of the traditional TS fuzzy multimodeling method is analyzed. Secondly, an active attraction-domain-search-based (AADSB) Lyapunov function construction method is proposed. The attraction domain index (ADI) is defined in this paper. The proposed method constructs Lyapunov function by searching the maximum ADI of studied systems. The position of the operating point which is corresponded to the maximum ADI is used to calculate the candidate energy function of studied systems. Thirdly, simulations and experiments are proposed to verified the effectiveness of the proposed method. Compared with the traditional TS fuzzy multimodeling method, the proposed method is less conservative and has less parameter selection restrictions.

Due to the high incidence of faults in submarine cables, the rapid and accurate fault line detection is crucial for ensuring the safety and reliable operation of submarine MVDC power systems. In this paper, considering the fault response of shore station MMC, the transient and steady-state fault characteristics of meshed submarine MVDC power systems are analyzed. It reveals the differences in the direction and amplitude of high frequency superimposed currents (HFSCs) and low frequency superimposed currents (LFSCs) at both ends of the fault and non-fault lines. Then, by extracting transient energy from HFSC and monitoring changing trend of LFSC, signed binary state direction features of full frequency current are derived. These features are used to construct the protection criteria, which are robust against the change of fault scenario, fault starting speed and sampling frequency. Localized feature processing renders the protection accuracy immune to communication delay and synchronization error. Furthermore, the low bandwidth fault data exchange demand between line terminals is ensured by combining the fast submarine cabled communication and flexible space-ground-sea integrated network. Finally, simulation results on the modified North East Pacific Time-Integrated Undersea Networked Experiments (NEPTUNE) power system validate the effectiveness of the proposed scheme under various fault scenarios.

Due to the high fault occurrence probability of submarine cable caused by extreme working environment, accurate fault detection and location is very essential for submarine DC power systems. While electrical faults are usually accompanied by the failures of optical fiber communication, resulting in the poor reliability of the existing methods based on cabled communication. Therefore, we propose a communication-free fault location method using multiple incipient current features. First, the fault modeling and current loop decoupling of submarine DC power systems are carried out to obtain the time domain description of fault line current. According to the varying trends and rates of incipient fault current with fault distance and fault resistance, multiple time domain features are utilized to reflect the fault distance meanwhile weaken the adversity of transition resistance. Then, the segmented mapping model between selected features and fault distance are established by using least squares support vector machine regression (LSSVR) algorithm. Via the integrated fault features and segmented location models, the proposed method can cover the fault location of whole line, and offers the advantages of fast speed and high accuracy. Finally, PSCAD simulation and hardware-in-loop (HIL) experiment results validate the effectiveness of the proposed method under various fault conditions.

Solid-state circuit breaker (SSCB) has the advantages of fast switching response, no arc, and flexible control in dc fault clearance. The bidirectional power flow of dc microgrid requires SSCBs based on power devices with unidirectional turn-OFF capability to reliably clear faults at either end. Thus, this article proposes a self-powered bidirectional SSCB (BSSCB) without an external auxiliary power supply. First, a self-powered BSSCB topology of silicon carbide junction field-effect transistors (SiC JFETs) is proposed. Then, the turn-OFF process of the proposed BSSCB is analyzed in detail, where the effects of self-powered capacitors and voltage-clamping metal oxide varistors (MOVs) are considered. It reveals the influence rules and parameter design basis of these components on turn-OFF time and overshoot voltage. Finally, a BSSCB prototype is constructed and tested. The experimental results demonstrate that the proposed BSSCB can rapidly isolate short-circuit faults. Additionally, it can maintain a safe and stable self-power supply whether the short-circuit fault occurs on any side of the BSSCB.

The fault ride through (FRT) control of inverter-based distributed generators (IBDGs) is quickly activated after a fault to limit the output current within 2 per unit. There is a phase jump phenomenon at the commencement of FRT control. However, the low fault current level and time-varying phase could make it difficult to guarantee the rapidity and the selectivity of islanded microgrid protection. In this paper, the transient characteristics of islanded microgrids are analyzed considering the influence of IBDG fault response and flexible network topology. The solution reveals the phase jump direction and magnitude difference between internal and external fault currents. Then, the energy value, the skewness value of directional morphological gradient, and peak polarity of incipient fault current are applied to formulate the protection criteria. The proposed protection scheme offers the following features: faulted phase selection capability, insensitivity to fault inception angle, adaptation to changes in the network topology, noise immunity with signal to noise ratio (SNR) down to 1dB, and low communication bandwidth requirements. The simulation results verify the effectiveness of proposed scheme for various fault conditions.

The transient responses of distributed energy resources (DERs) in a microgrid are dynamically correlated in spatial and temporal dimensions. Hence, the transient stability prediction in microgrids would require an effective modeling of time-varying correlations and the mining of spatial–temporal features of electrical data. This paper proposes a refined DER-level transient stability prediction method for microgrids considering the time-varying spatial–temporal correlations of DERs. First, the spatial–temporal dynamic correlation of DERs was extracted and modeled by an attention-based mechanism. Then, a spatial–temporal graph convolution network was proposed to predict the dynamics of unstable DERs and the instability severity trend in a microgrid. The TSP model consisted of three parts: (1) several stacked spatial–temporal convolution modules to simultaneously mine the spatial–temporal dynamic features of microgrids, (2) an unstable DER identification module to predict the microgrid system stability and identify unstable DERs, and (3) an instability severity trend prediction module for DERs in a microgrid. The test results on a realistic 16-bus 10-DER microgrid demonstrated that the proposed prediction method possessed the desirable reliability and interpretability and outperformed the state-of-the-art baselines in unstable DER identifications and DER instability severity trend predictions.

Considering single-phase to ground (SPG) faults accounting for more than 80% of all faults, neutral ineffectively grounded mode (NIGM) is the most promising choice for medium voltage (MV) islanded microgrids (MGs) to enhance operation reliability. But due to the weak fault current and flexible network topology of islanded MGs, it is difficult to detect SPG fault lines. To solve this issue, this paper firstly analyzes the SPG fault characteristics of the MV islanded MGs with different NIGMs and network topologies. It is found that the looped MGs have a virtual terminal, i.e., the point where zero-sequence current (ZSC) is zero. And the feature differences between fault and non-fault lines are revealed. Based on that, an SPG fault line identification method is proposed. The main criterion is constructed based on the skewness signs of high frequency zero-sequence voltage (ZSV) and ZSC. Additionally, the fundamental amplitude of the difference between the actual ZSC received from another terminal and the ZSC calculated from local ZSV and ZSC is used to formulate an auxiliary criterion. Finally, the effectiveness of the proposed method is validated in a 10kV islanded MG model considering different NIGMs, different fault conditions, arc grounding faults, and the influences of the line parameter, operating point and measurement noises.