Building a voltage governance framework for modern power systems: challenges, practices, and prospects

During the development of modern power systems, the “dual-high” characteristics—high penetration of renewable energy and high power-electronic interfacing—have become increasingly prominent in power grids. Consequently, voltage issues have evolved from localized, technical challenges into systemic and multi-level risks to safety and stability. In this context, and in light of the structural transformation and operational characteristics of the modern power systems under the “dual-carbon” goals, this paper systematically analyzes the causes and manifestations of bidirectional voltage violations, pronounced power quality problems, reduced voltage stability margins, and rapid reactive power fluctuations. The paper further examines the multiple challenges currently faced in voltage governance, including unclear voltage regulation mechanisms, insufficient reactive power regulation resources, and a lack of coordinated control mechanisms. Finally, key technologies and practical experiences for building a voltage governance framework are presented, aiming to provide theoretical insights and practical pathways for the development of secure, high-quality modern power systems.

A voltage control strategy for AC/DC hybrid distribution networks based on improved active disturbance rejection control and fuzzy neural networks

With the increasing penetration of distributed photovoltaic and wind power in active distribution networks, system power fluctuations are intensified, leading to frequent voltage violations on both AC and DC buses and posing challenges to the safe and stable operation of distribution networks. To address this issue, an AC/DC hybrid active distribution network with a high proportion of wind, photovoltaic, and energy storage resources is taken as the application scenario. A system model is developed that includes photovoltaic units, energy storage systems, wind turbines, loads, and bidirectional converters. The mechanism by which power fluctuations influence distribution network voltage is analyzed. Based on this, an improved distributed voltage coordinated control strategy is proposed. On the DC side, an active disturbance rejection control scheme based on an error-driven adaptive extended state observer is adopted to enhance the estimation and compensation capability for time-varying disturbances. On the AC side, a fuzzy neural network controller is designed to achieve adaptive optimization of inverter voltage loop parameters. Meanwhile, a power feedforward mechanism is introduced to transmit AC-side fluctuation information to the DC-side energy storage controller, enabling coordinated regulation between the AC and DC subsystems. Finally, simulation results based on MATLAB/Simulink verify the feasibility and effectiveness of the proposed strategy in improving voltage stability in active distribution networks.

A voltage regulation method for active power reserve-based grid-forming photovoltaic inverters

The increasing penetration of distributed photovoltaic(PV) systems in low-voltage distribution areas has led to growing voltage violations due to source-load mismatch and inadequate reactive power support. To address this, a voltage regulation method for active power reserve-based grid-forming(GFM) photovoltaic(PV) inverters is proposed. In the grid-forming virtual synchronous generator(VSG) inverter control framework, multiple modes such as maximum power tracking/active power reserve, reactive power adaptive voltage regulation/reactive power locking are introduced, forming a coordinated voltage regulation logic with “reactive power regulation prioritized and flexible active power as backup”. A small-signal model is established using the state-space method to identify the dominant poles and provide parameter tuning criteria. The symmetrical component method is used to construct the positive and negative sequence output impedance, and the impedance ratio criterion is applied to evaluate grid connection stability. A fine-tuned model of a typical voltage violation area is built on the Simulink platform, and the proposed control strategy is integrated for simulation verification. The results show that this strategy can quickly release the voltage regulation margin in the early stage of voltage violation, effectively implement flexible active and reactive power adaptive coordinated voltage regulation, suppress voltage rise at the grid connection point, and provide voltage and frequency support. It is suitable for both existing and new distribution areas.

Installation locations of surge arresters on double-circuit transmission lines

The connected series-compensated capacitors alter the sequence network impedance of transmission lines and the distribution of sequence currents during faults. While this does not adversely affect the phase-selection elements on the capacitor installation side, it may lead to misidentification by those on the opposite side. To address this, a fault phase selection method based on line-mode phase current is proposed. First, the line-mode phase current is defined as the difference between the fault-component current of each phase and the zero-sequence current. Next, the characteristics of line-mode phase currents under different fault types are analyzed to extract distinctions between faulted and non-faulted phases, from which the phase-selection criterion is constructed. Finally, the method is validated through simulations on the PSCAD platform. The results show that the proposed method can accurately identify the faulted phase in series-compensated transmission lines, and its performance is robust with respect to fault location, line length, and the degree of series compensation.

An inference method for sparse measurements in distribution networks based on a graph imputation neural network

Incomplete deployment of measurement devices and data transmission losses can result in sparse measurements in distribution networks. To address this issue, this paper proposes an inference method for sparse measurements based on a graph imputation neural network(GINN). The proposed method aims to improve the accuracy and reduce the sparsity of existing measurements. First, a GINN-based measurement feature encoder module is designed to extract power flow features such as power and voltage from nodal measurements. A transformer network is employed to model cross-feature correlations among different power flow features. Second, a GINN-based graph encoder module explicitly encodes topological connectivity between distribution network nodes. By incorporating a graph convolutional network(GCN), this module enables the propagation and updating of nodal power flow features. Subsequently, by leveraging two modules to capture the correlations between different power flow features of node measurements and the topological correlations across nodes, the missing data is inferred and completed using the sparse measurements. Finally, simulation tests are conducted on IEEE 14-, 30-, 57-, and 118-bus systems to validate the effectiveness of the proposed method.

A multi-timescale photovoltaic power prediction method based on SE-CNN-BiLSTM and improved Transformer

The random and volatile distributed photovoltaic(PV) power pose challenges for accurate forecasting and dispatch decision-making in power system operations. To address this, A multi-timescale photovoltaic power prediction method based on SE-CNN-BiLSTM and improved Transformer is proposed. Firstly, leveraging the diurnal trend similarity characteristics of PV power, a feature extraction method incorporating a channel attention mechanism is proposed to construct a prediction model for PV power trend features. Subsequently, based on the short-term fluctuation characteristics of PV power, a fluctuation feature extraction method based on similar time-period matching(STM) is proposed, utilizing the weather-induced fluctuation features of PV power to build a prediction model based on an improved Transformer. Then, by fusing the long-and short-timescale trend features and fluctuation features of PV power, a multi-timescale fusion method for PV power prediction is constructed. Finally, the proposed model is validated using actual operational data from a PV power station and simulation data. Results demonstrate that the proposed method effectively enhances the representational capacity and prediction accuracy of the forecasting model.

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