High-voltage direct current (HVDC) grids require fast and reliable protection of the DC lines.The performance of traditional protection schemes is easily impaired by the limitations of the boundary condition and nonlinearity from the control of converters.One of the key technologies for flexible HVDC grids is the half-bridge modular multilevel converter (HB-MMC).Considering the high controllability of HB-MMC, this study proposes an active injection protection scheme to improve the reliability and sensitivity of the HVDC grid protection.The HB-MMC is used to inject a sinusoidal characteristic signal, at the specified frequency, into the DC lines.Then, the voltage and current at the specified frequency are extracted using the Prony algorithm to calculate the input impedance, which is used for the identification of internal and external faults.The active injection protection scheme was simulated for various cases in the simulation software Power Systems Computer Aided Design.The simulation results indicate that the proposed protection scheme is highly reliable and can overcome transition resistance.

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In this paper, a fault location method for the petal-shaped distribution network (PSDN) with inverter-interfaced distributed generators (IIDGs) is proposed to shorten the time of manual inspection.In order to calculate the fault position, the closed-loop structure of the PSDN is skillfully exploited, and the common control strategies of IIDGs are considered.For asymmetrical faults, a fault line identification formula based on the negative-sequence current phase differences is presented, and a fault location formula only utilizing the negative-sequence current amplitudes is derived to calculated the fault position.For symmetrical faults, the positive-sequence current at both ends of lines and the current output from IIDGs are used to identify the fault line, and the positive-sequence current on multiple lines are used to pinpoint the fault position.In this method, corresponding current phasors are separated into amplitudes and phases to satisfy the limitation of communication level.The simulation results show that the error is generally less than 1%, and the accuracy of the proposed method is not affected by the fault type, fault position, fault resistance, load current, and the IIDG penetration.

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The existing LCC-HVDC transmission project adopts the fixed-time delay restarting method.This method has disadvantages such as non-selectivity, long restart process, and high probability of restart failure.These issues cause a secondary impact on equipment and system power fluctuation.To solve this problem, an adaptive restarting method based on the principle of fault location by current injection is proposed.First, an additional control strategy is proposed to inject a current detection signal.Second, the propagation law of the current signal in the line is analyzed based on the distributed parameter model of transmission line.Finally, a method for identifying fault properties based on the principle of fault location is proposed.The method fully considers the influence of the long-distance transmission line with earth capacitance and overcomes the influence of the increasing effect of the opposite terminal.Simulation results show that the proposed method can accurately identify the fault properties under various complex fault conditions and subsequently realize the adaptive restarting process.

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Definite-time zero-sequence over-current protection is presently used in systems whose neutral point is grounded by a low resistance (low-resistance grounding systems).These systems frequently malfunction owing to their high settings of the action value when a high-impedance grounding fault occurs.In this study, the relationship between the zero-sequence currents of each feeder and the neutral branch was analyzed.Then, a grounding protection method was proposed on the basis of the zero-sequence current ratio coefficient.It is defined as the ratio of the zero-sequence current of the feeder to that of the neutral branch.Nonetheless, both zero-sequence voltage and zero-sequence current are affected by the transition resistance, The influence of transition resistance can be eliminated by calculating this coefficient.Therefore, a method based on the zero-sequence current ratio coefficient was proposed considering the significant difference between the faulty feeder and healthy feeder.Furthermore, unbalanced current can be prevented by setting the starting current.PSCAD simulation results reveal that the proposed method shows high reliability and sensitivity when a high-resistance grounding fault occurs.

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For the load modeling of a large power grid, the large number of substations covered by it must be segregated into several categories and, thereafter, a load model built for each type.To address the problem of skewed clustering tree in the classical hierarchical clustering method used for categorizing substations, a fair hierarchical clustering method is proposed in this paper.First, the fairness index is defined based on the Gini coefficient.Thereafter, a hierarchical clustering method is proposed based on the fairness index.Finally, the clustering results are evaluated using the contour coefficient and the t-SNE two-dimensional plane map.The substations clustering example of a real large power grid considered in this paper illustrates that the proposed fair hierarchical clustering method can effectively address the problem of the skewed clustering tree with high accuracy.

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In this paper, the electrical PageRank method is proposed to identify the critical nodes in a power grid considering cascading faults as well as directional weighting.This method can rapidly and accurately focus on the critical nodes in the power system.First, the proposed method simulates the scenario in a grid after a node is attacked by cascading faults.The load loss of the grid is calculated.Second, the electrical PageRank algorithm is proposed.The nodal importance of a grid is determined by considering cascading faults as well as directional weights.The electrical PageRank values of the system nodes are obtained based on the proposed electrical PageRank algorithm and ranked to identify the critical nodes in a grid.Finally, the effectiveness of the proposed method is verified using the IEEE39 node system.The proposed method is highly effective in preventing the occurrence of cascading faults in power systems.

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It is crucial to maintain the safe and stable operation of distribution transformers, which constitute a key part of power systems.In the event of transformer failure, the fault type must be diagnosed in a timely and accurate manner.To this end, a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein.First, we perform feature extraction on the collected infrared-image data to extract temperature, texture, and shape features as the model reference vectors.Then, a generative adversarial network (GAN) is constructed to generate synthetic samples for the minority subset of labelled samples.The proposed method can learn information from unlabeled sample data, unlike conventional supervised learning methods.Subsequently, a semi-supervised graph model is trained on the entire dataset, i.e., both labeled and unlabeled data.Finally, we test the proposed model on an actual dataset collected from a Chinese electricity provider.The experimental results show that the use of feature extraction, sample generation, and semi-supervised learning model can improve the accuracy of transformer fault classification.This verifies the effectiveness of the proposed method.

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Compensating for photovoltaic (PV) power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty, it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper, a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed, and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system; the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

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The technology deployed for lithium-ion battery state of charge (SOC) estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries, thereby improving discharge efficiency and extending cycle life.In this study, the key lithium-ion battery SOC estimation technologies are summarized.First, the research status of lithium-ion battery modeling is introduced.Second, the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third, the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally, the current research problems and prospects for development trends are summarized.

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As the demand for customer service continues to increase, more companies are attempting to apply artificial intelligence technology in the field of customer service, enabling intelligent customer service, reducing customer service pressure, and reducing operating costs.Currently, the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions, and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding, this paper proposes a semantic understanding model based on the fusion of a convolutional neural network (CNN) and attention.The model builds an “intention-slot” joint model based on the “encoding-decoding” framework and uses hidden semantic information that combines intent recognition and slot filling, avoiding the problem of information loss in traditional isolated tasks, and achieving end-to-end semantic understanding.Additionally, an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text, thereby increasing the accuracy of semantic understanding.Finally, by applying the model to electric power intelligent customer service, we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

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