A power system stabilizer (PSS) is a control system integrated into the control structure of specific generation units within AC grids. It monitors current, voltage, and machine shaft speed. Analysing these variables, the PSS generates appropriate control signals to the voltage regulator unit, aiming to damp system oscillations. With the advancement of high‐voltage direct current (HVDC) overlaid high‐voltage alternative current (HVAC) grids, it is anticipated that direct current power system stabilizers (DC‐PSS) will be developed to perform a similar role as their AC counterparts. DC‐PSS will be responsible for monitoring and controlling DC voltage levels, ensuring stable operations. This paper focuses on DC‐PSS in HVDC grids, designed to ensure stable operation and mitigate voltage fluctuations. Unlike conventional AC power systems, HVDC includes only DC voltage and power. The input signal for DC‐PSS is the variations in DC voltage, and the output signal is proportional to the power changes at the specific bus where the DC‐PSS is installed, aiming to minimize DC voltage oscillations. These characteristics pose significant challenges in DC‐PSS. The paper addresses the challenges and highlights issues such as inertia and low‐frequency oscillations associated with DC‐PSS. Various control methods are presented and a comparison is made among these methods.

Direct current power system stabilizer (DC‐PSS) is used to improve the performance and the efficiency of the power converters. The main benefits of using DC‐PSS include lower power loss, better efficiency, and reduced stress on the power conversion components. It also helps improve the dynamic response of the power converter system.image

The commutation failure is the most prevalent fault in line‐commutated converter based high‐voltage direct current systems, which may result in transient overvoltage on the sending‐side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large‐scale wind turbines and implement effective stability control measures. In this paper, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented firstly, along with an analytical expression for the peak value of transient overvoltage. Secondly, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. On this basis, an overvoltage analysis method integrating the model‐driven and data‐driven techniques is proposed, and the improved DT algorithm is applied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the simulation system of Northwest China local power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method.

Decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. To compensate for potential inaccuracies in the data‐driven method, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented. On this basis, an overvoltage analysis method integrating the model‐driven and data‐driven techniques is proposed, and the improved DT algorithm is applied to fast error correction, enhancing the interpretability of regression prediction results. image

The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy.

Power quality classifications are carried out by noise level‐based disturbance signal classification in a traditional manner. This paper proposes the classification of power quality disturbance using a deep convolution neural network (DCNN) with a bidirectional long short‐term memory (BiLSTM) layer for further extraction and significant classification on a real‐time system.image

To fully utilize the voltage regulation capacity and interaction characteristics of the Transmission and Distribution (T&D) system, a novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response (DR) and cooperation with the Transmission Network . The proposed scheme consists of two vital components: an MPOMDP block and a Modified Asynchronous Advantage Actor‐Critic‐based RL block. The MPOMDP block innovatively exploits the confidence interval of the observed state to make a better perception of the precise system state by introducing two new probability vectors. Then the MPOMDP block is fed into the underlying architecture of the RL block for asynchronously capturing features and optimal decision‐making, where the solving framework additionally brings in a public data buffer to realize boundary information sharing. Case studies are conducted on a modified T&D system considering N‐1 contingencies, with a training dataset from a district in Suzhou, China. Simulation results demonstrate that the proposed scheme can achieve significant voltage optimization while ensuring fast convergence speed.

A novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response and cooperation with the Transmission Network. The proposed scheme consists of two vital components: an MPOMDP block which makes a better perception of the precise system state and a Modified Asynchronous Advantage Actor‐Critic‐based RL block which assists agents asynchronously to capture features and generate optimal decision‐making.image

Low and medium speed magnetic levitation traffic with short power supply distance and complex grounding network structure, prone to power supply rail grounding faults. However, the existing fault location methods do not accurately locate the fault point, making it difficult for the protective device to act to cut off the fault. To address the above problems, this paper builds a dynamic simulation model of the low and medium speed magnetic levitation power supply rails to study the distribution characteristics of the fault traveling waves after a ground fault occurs in the power supply rails, and analyses the generation mechanism of the traveling wave spectrum through formula calculation. First, the difference in current between the positive and negative bus bars is analyzed to determine whether a ground fault has occurred. Second, the direction of the current difference between stations is compared to locate the faulty section. Finally, the fault distance is calculated from the frequency difference of the fault voltage at the double‐ended station. Through simulation, the method is validated to be unaffected by fault location, fault transition resistance, noise interference, and is applicable to short circuit faults caused by lightning strikes, and the ranging error always remains within 20 m. The method has strong robustness, can effectively solve the problem of protection misoperation and accurately locate the fault point. It is suitable for low and medium speed magnetic levitation transportation power supply rail ground fault.

A dynamic simulation model of the low and medium speed magnetic levitation power supply rails is built to study the distribution characteristics of the fault traveling waves after an earth fault occurs on the power supply rails, and the generation mechanism of the traveling wave spectrum is analysed by means of equation calculations.

The difference between the positive and negative bus currents is analysed to locate the fault section, and the fault distance is calculated from the frequency difference of the fault voltage at the double‐ended station.

The article method is not affected by fault location, fault transition resistance, noise interference and is applicable to short circuit faults caused by lightning strikes. image