Abstract:The integration of inverter-interfaced distributed generations (IIDGs) has modified the fault characteristics of active distribution networks (ADNs), substantially affecting the range, sensitivity, and reliability of conventional single-ended protection. Conventional current differential protection and existing waveform similarity-based protection methods suffer from poor tolerance to transition resistance and rely on simulations or field experiences for setting threshold values. To address these problems, this paper proposes a pilot protection method based on improved Fréchet distance by analyzing the differences in amplitude-phase characteristics of short-circuit currents during internal and external faults in ADNs with IIDGs. This method fully considers the physical significance of applying Fréchet distance to power-frequency current waveforms, offering a short operating time, high tolerance to transition resistance, and strong resistance to synchronization errors. Furthermore, a compensation criterion based on the positive-sequence current amplitude of the virtual T-connected branch is established to handle the impact of multiple unmeasurable T-connected branches. PSCAD simulations verify the effectiveness of the proposed method under different fault conditions and demonstrate its improvement over existing methods. Finally, a real-time digital simulator (RTDS)-based hardware test platform is used to further verify the developed differential relay prototypes based on the proposed method.

Abstract:In self-excited induction generator (SEIG)-based wind energy systems, voltage and frequency fluctuate with variations in wind speed and load, reducing power quality and efficiency. A distribution static synchronous compensator (DSTATCOM) is an effective solution to mitigate these fluctuations but requires real-time and accurate control, including precise estimation of voltage and current parameters. This paper proposes a fast hybrid-phase locked loop (FH-PLL)-based DSTATCOM control algorithm, offering superior filtering capabilities and enhanced sensitivity in detecting amplitude, frequency, and phase angle variations. The proposed method significantly improves the performance of DSTATCOM-assisted SEIG energy systems. Unlike conventional alternatives, which often suffer from either low estimation accuracy or high computational complexity, the proposed approach achieves an optimal balance between computational efficiency and estimation precision, making it a superior alternative to existing control algorithms. Comprehensive comparative performance evaluations under various challenging conditions such as non-linear loads, unbalanced loads, open-circuit faults, and measurement offsets, demonstrate that the proposed method achieves the lowest total harmonic distortion (THD) and total demand distortion (TDD) compared to state-of-the-art techniques, including the enhanced phase locked loop (EPLL), second-order generalized integrator (SOGI), and conventional synchronous reference frame PLL (SRF-PLL), while remaining compliant with the relevant IEEE 519-2014 standards.

Abstract:With the increase of adjustable loads connected to power systems, there is a growing risk of intentionally exploiting load adjustability to cause power balance deviations. This poses an obvious threat to power system frequency, which has been noticed by many system operators. However, the current dispatch method cannot consider the attack of load on frequency, let alone providing a preventive dispatch decision that reduces the frequency deviation. To address this challenge, a preventive dispatch method that mitigates the impact of adjustable load on power system frequency is presented for the first time. The relationship between the time-varying power mismatch caused by the attack of adjustable load and frequency deviation is established. This is achieved by theoretically deriving the transfer function between system frequency and power mismatch in time domain and discretizing the function into several segments according to the division of time horizons. A formal analysis of the error caused by the transformation is performed, which provides quantitative guidance on the accuracy of the frequency modeling. Based on this, the worst-case scenario of frequency deviation with adjustable load is determined through solving an optimization model. Then, a novel preventive dispatch method that guarantees the system frequency security under the worst-case scenario is presented. Particularly, the generator ramping behavior after receiving the AGC adjustment command is modeled by a group of linear constraints to distinguish the load tracking abilities of generators. Case studies based on the IEEE30-bus system and a 661-bus utility system show that the proposed preventive dispatch method can achieve a 5.8%-19.42% improvement of the maximum absolute frequency deviation.

Abstract:With the increasing penetration of large-scale renewable energy sources into the power grid, distribution networks are facing significant challenges, including intensified voltage fluctuations and increased network losses. Although deep reinforcement learning has made considerable advancements in addressing optimization problems compared to traditional algorithms, there has been limited focus on enhancing convergence and safety in cooperative optimization scenarios, particularly those involving topological reconstruction. To overcome these challenges, this paper proposes a distribution network optimization model that incorporates topological security-constrained integrated reinforcement learning. The model improves the encoding of topologies by representing them in a multi-dimensional discrete space and introduces a topological masking mechanism to achieve high safety and computational efficiency. Additionally, an ensemble strategy is utilized to develop an action network group, improving action prediction and screening, thereby achieving better training stability. Experiments conducted on an enhanced IEEE33-node distribution network system indicate that the proposed improvements significantly enhance training stability and support the safe and efficient operation of the system.

Abstract:Harmonic state estimation in distribution networks is essential for identifying harmonic sources. However, issues such as limited measurement redundancy, asynchronous measurements, and unbalanced load distributions in three-phase networks undermine the reliability of existing methods in practical applications. To address these issues, this paper proposes an interval harmonic state estimation method in three-phase unbalanced distribution networks, integrating data from multiple sources. First, the interval multi-source harmonic measurement dataset is constructed by integrating asynchronous harmonic measurement data from multiple sources. The time asynchrony of measurement data from power quality monitoring devices is calibrated using the sliding window weighted dynamic time warping algorithm. Second, the interval harmonic state estimation model for the three-phase asymmetric distribution network is constructed. The model is solved using the interval-weighted least squares method, enhanced by the improved Krawczyk operator, thereby minimizing the expansion resulting from interval operations. Finally, the feasibility and accuracy of the proposed interval harmonic state estimation method are validated.

Abstract:To ensure safety while maintaining efficient operation and fast power regulation, this paper proposes a coordinated control strategy for variable-speed pumped storage units with full-size converters (FSC-VSPSU) in generation state. First, the dynamic stability characteristics and the key influencing factors in the fast power control mode and fast speed control mode are analyzed using the eigenvalue analysis method. The results indicate that the fast speed control mode can effectively address the issue of ultra-low frequency oscillation induced by fast power control. Then, the characteristics among the optimal speed, guide vane opening, and efficiency are clarified through laboratory testing of a pump turbine model machine, laying the foundations for developing optimized operation for the pumped storage units. Based on the analysis of slow load regulation and power step regulation, the power frequency regulation characteristics of the two control modes are revealed. Finally, leveraging the advantages of dynamic stability and power-speed regulation ability under the two control modes, a novel coordinated control strategy is proposed. The strategy has been applied to the first FSC-VSPSU in China, and the field tests demonstrate that the unit achieves excellent speed regulation performance and fast power regulation capability.

Abstract:Energy storage batteries operating under high levels of renewable energy integration face significant power fluctuations and frequent charge-discharge cycles, leading to substantial errors and uncertainties in state-of-charge (SOC) estimation at short time scales. To address this challenge, this paper proposes a novel SOC estimation method by integrating adaptive forgetting factor recursive least squares (AFF-RLS) with a data-driven hybrid architecture based on bidirectional long short-term memory (BiLSTM) and Transformer model. A second-order equivalent RC circuit model is constructed, and AFF-RLS is employed for real-time identification of model parameters, which are subsequently used as input features for the BiLSTM-Transformer model. The learning rate is dynamically adjusted based on error variation, and network parameters are optimized using the Adam algorithm. The method is validated using experimental data obtained from lead-carbon batteries, with its reliability and robustness verified through widely accepted performance metrics, including mean absolute error, mean absolute percentage error, root mean square error, and the coefficient of determination. Comparative experiments against convolutional neural network, Transformer, and LSTM-based models indicate that the proposed SOC estimation method consistently achieves lower estimation errors within 1.5% across varying state-of-health, demonstrating superior accuracy and robustness.

Abstract:Recognizing the intricate spatiotemporal correlation (STC) among wind farms (WFs) is critical to achieving better predictions for wind farm clusters (WFCs). To describe the STC accurately, this paper employs the wind angular field method to transform the wind series of WFs into different 2-D feature maps, and then construct homogeneous and heterogeneous STC graphs from these maps. The graphs are dynamically updated at the frequency of data update to capture time-varying STC among WFs. Finally, a dynamic graph attention network, designed according to the STC graphs, is established for WFC prediction. Through the above process, dynamic and accurate descriptions of STC are realized in WFC prediction. From the case study of a large-scale WFC with a capacity over 5800 MW in Northeast China, the proposed method reduces the root mean square error of the prediction in the next 24 hours by 2.67%.

Abstract:Fault currents contributed by inverter-based resources (IBRs) differ fundamentally from those of conventional sources. As a result, many traditional protection functions, such as directional elements, may fail to operate correctly when faults are predominantly supplied by these IBRs. This paper analyzes the relative angle between positive-sequence voltage and current under symmetrical faults and evaluates its variation at both near-end and far-end buses. In addition, the relative angle between negative-sequence voltage and current is analytically derived for fault currents supplied by different IBR control strategies. Based on this analysis, a unified directional element is designed that operates reliably for both symmetric and asymmetric faults, across a wide range of fault locations and resistances, and independently of the IBR controller employed. By addressing the unique challenges presented by IBRs, this research aims to enhance the reliability and effectiveness of protection systems in modern power grids. Time-domain simulations demonstrate the accuracy of the proposed directional element in correctly identifying fault directions across a wide range of fault locations and resistances, as well as under different IBR control strategies.

Abstract:Transient issues arising from voltage drops in distribution networks with high penetration of renewable energy, as well as post-fault reclosing challenges, are often addressed separately through low-voltage ride-through (LVRT) and quasi-simultaneous control, without a comprehensive stability analysis covering the entire fault stage. This paper reveals the temporal correlation between LVRT control and quasi-simultaneous control and proposes an integrated optimal control strategy. The strategy enhances the transient stability of droop-controlled inverters following voltage drops, ensures reliable load supply during off-grid operation, and achieves seamless grid reconnection without current surges during reclosing. During the LVRT phase, the inverter power reference is dynamically adjusted based on power angle characteristics and reactive power-current relationships to stabilize the power angle and limit output current. In the reclosing transition phase, a combined frequency and phase angle regulation mechanism is employed to minimize discrepancies in voltage frequency and phase angle, accounting for transient operating condition changes. The effectiveness and practicality of the proposed strategy are validated through simulations and experimental results.

Abstract:Determining the coupling matrix is crucial for reconstructing transient voltages on overhead transmission lines using non-contact measurement techniques. However, existing methods often fail to account for the effects of asymmetrical sensor placement and the presence of multiple adjacent conductors in substations. This paper proposes an optimization-based approach to estimate the coupling matrix elements, aiming to minimize the transient voltage reconstruction error, with the measured power frequency voltage imposed as a constraint. The influence of adjacent conductors on the measured electric field is assumed to have a waveform similar to that of the measured conductors, as ensured by the electrically small condition during switching transients. The method is applied to reconstruct the closing and opening transient voltage waveforms of a 500 kV overhead transmission line. The estimated coupling matrix coefficients exhibit consistent performance across 11 tests, with maximum estimation errors of 6.2% and 2.5% for the opening and closing transient voltages, respectively. Moreover, the results indicate that the low-frequency response characteristics of capacitive voltage transformers (CVTs) are insufficient for capturing the opening transient voltages on transmission lines. By leveraging the full waveform of the measured transient electric field signal, the proposed method eliminates the need for symmetrical sensors and ensures robust field performance.

Abstract:In the decarbonization in the electricity sector, the emergence of prosumers in the medium/low voltage power distribution systems poses significant challenges for distribution system operators (DSOs), as bidirectional power exchange and carbon trading occur more frequently. This paper focuses the coordinated electricity and carbon management in a bi-level frame-work for DSOs and local distributed resources aggregators (DRAs). At the upper level, the optimal power flow (OPF) and carbon emission flow model (CEF) are solved to derive the locational marginal price (LMP) and nodal carbon intensity (NCI) for DRAs. At the lower level, an exponential-based pricing model is designed to enable peer-to-peer (P2P) carbon emission permit (CEP) trading among DRAs, which involves CEP demand-supply situation and emission assessment pressure. Chance constraints are formulated to address uncertainties in DRAs. Numerical results confirm the effectiveness of the proposed solution in coordinated electricity and carbon operation of multi-stakeholders in power distribution systems.