Search Results (74)

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids. Full article

The Dynamic Voltage Restorer (DVR), which is connected in series between the power grid and the load, can rapidly compensate for voltage disturbances to maintain stable voltage at the load end. To enhance the energy supply capacity of the DVR and utilize its shared circuit topology with photovoltaic (PV) inverters—which enables the dual functions of voltage compensation and PV-storage power generation—this study integrates PV and energy storage as a coordinated energy unit into the DVR, forming a PV-storage-integrated DVR system. The core innovation of this system lies in extending the voltage disturbance detection capability of the DVR to include harmonics. By incorporating a Butterworth filtering module and voltage fluctuation tracking technology, high-precision disturbance identification is achieved, thereby supporting power balance control and functional coordination. Furthermore, a multi-mode-power coordinated regulation method is proposed, enabling dynamic switching between operating modes based on PV output. Simulation and experimental results demonstrate that the proposed system and strategy enable smooth mode transitions. This approach not only ensures reliable voltage compensation for sensitive loads but also enhances the grid-support capability of PV systems, offering an innovative technical solution for the integration of renewable energy and power quality management. Full article

To meet the demands of deep grid integration of renewable energy and long-distance power transmission, this paper presents a hybrid multi-infeed DC system architecture that includes an AC power source (AC), a voltage source converter (VSC), and a modular multilevel converter (MMC). Addressing the limitations of traditional differential protection—such as insufficient sensitivity under high-resistance grounding and susceptibility to false operations under out-of-zone disturbances—this paper introduces an enhanced current differential criterion based on dynamic phasor analysis. By effectively decoupling DC bias and load current components and optimizing the calculation of action and braking quantities, the proposed method enables the rapid and accurate identification of typical faults, including high-resistance grounding, three-phase short circuits, and out-of-zone faults. A multi-scenario simulation platform is built using MATLAB to thoroughly validate the improved criterion. Simulation results demonstrate that the proposed method offers excellent sensitivity, selectivity, and resistance to false operations in multi-infeed complex systems. It achieves fast fault detection (~2.0 ms), strong sensitivity to high-resistance internal faults, and low false tripping under a variety of test scenarios, providing robust support for next-generation DC protection systems. Full article

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article

This work presents the design of a control algorithm based on an augmented incremental state-space model, emphasizing its compatibility with Takagi–Sugeno (T–S) fuzzy models for nonlinear systems. The methodology integrates key components such as incremental modeling, fuzzy system identification, discrete Linear Quadratic Regulator (LQR) design, and state observer implementation. To optimize controller performance, the Extremum Seeking Control (ESC) technique is employed for the automatic tuning of LQR gains, minimizing a predefined cost function. The control strategy is formulated within a generalized framework that evolves from conventional discrete fuzzy models to a higher-order incremental-N state-space representation. The simulation results on a nonlinear multivariable thermal mixing tank system validate the effectiveness of the proposed approach under reference tracking and various disturbance scenarios, including ramp, parabolic, and higher-order polynomial signals. The main contribution of this work is that the proposed scheme achieves zero steady-state error for reference inputs and disturbances up to order N−1 by employing the incremental-N formulation. Furthermore, the system exhibits robustness against input and load disturbances, as well as measurement noise. Remarkably, the ESC algorithm maintains its effectiveness even when noise is present in the system output. Additionally, the proposed incremental-N model is applicable to fast dynamic systems, provided that the system dynamics are accurately identified and the model is discretized using a suitable sampling rate. This makes the approach particularly relevant for control applications in electrical systems, where handling high-order reference signals and disturbances is critical. The incremental formulation, thus, offers a practical and effective framework for achieving high-performance control in both slow and fast nonlinear multivariable processes. Full article

Conventional model-free control (MFC) is widely used in motor drives due to its simplicity and model independence, yet its performance suffers from imperfect disturbance estimation and input gain mismatch. To address these issues, this paper proposes an adaptive enhanced model-free speed control (AEMFSC) scheme based on an ultra-local model for permanent magnet synchronous motor (PMSM) drives. First, by integrating a nonlinear disturbance observer (NDOB) and a PD control law into the generalized model-free controller, an enhanced model-free speed controller (EMFSC) was developed to ensure closed-loop stability. Compared with a conventional MFSC, the proposed method eliminated steady-state errors, reduced the speed overshoot, and achieved faster settling with improved disturbance rejection. Second, to address the performance degradation induced by input gain $\alpha$ mismatch during time-varying load conditions, we developed an online parameter identification method for real-time $\alpha$ estimation. This adaptive mechanism enabled automatic controller parameter adjustment, which significantly enhanced the transient tracking performance of the PMSM drive. Furthermore, an algebraic-framework-based high-precision identification technique is proposed to optimize the initial $\alpha$ selection, which effectively reduces the parameter tuning effort. Simulation and experimental results demonstrated that the proposed AEMFSC significantly enhanced the PMSM’s robustness against load torque variations and parameter uncertainties. Full article

This study focuses on a new identification method for moving loads on bridge structures using field-based displacement data from different measurement points on a continuous rigid-frame bridge. A novel approach has been proposed to make use of the area of the absolute field value derived from the displacement influence line of continuous rigid-frame bridges. Considering the potential presence of other nuisance loads (i.e., noise) on the bridge, this method can significantly mitigate the impact of noise by adopting the absolute area method of influence lines. In addition, the new method combines data from various field measurement points to identify the moving loads, which can in turn minimize the influence of measurement errors. To validate the new method, several numerical simulations varying different noises and parameters have been carried out for benchmarking. The results show that our proposed method achieves an outstanding identification accuracy of over 95% for the simulation cases with the disturbance noise amplitude less than 1.0% and the field data with random noise. This new method enables the identification of moving loads on bridges, thereby providing fundamental data for bridge health monitoring and damage detection. This will help improve predictability of the remaining fatigue life of bridge structures. Full article

The growing complexity and uncertainty in modern power systems—driven by increased integration of renewable energy sources and variable loads—underscore the need for robust tools to assess dynamic stability. This paper presents an enhanced methodology for modal analysis that combines Adaptive Variational Mode Decomposition (A-VMD) with Prony’s method. A novel energy-based selection mechanism is introduced to determine the optimal number of intrinsic mode functions (IMFs), improving the decomposition’s adaptability and precision. The resulting modes are analyzed to estimate modal frequencies and damping ratios. Validation is conducted using both synthetic datasets and real synchrophasor measurements from Ecuador’s national power grid under ambient and disturbed operating conditions. The proposed approach is benchmarked against established techniques, including a matrix pencil, conventional VMD-Prony, and commercial tools such as WAProtector and DIgSILENT PowerFactory. The results demonstrate that A-VMD consistently delivers more accurate and robust performance, especially for low signal-to-noise ratios and low-energy ambient conditions. These findings highlight the method’s potential for real-time oscillation mode identification and small-signal stability monitoring in wide-area power systems. Full article

The increasing number of power electronic devices in power networks causes a significant increase in supraharmonics in these networks. Supraharmonics are spectral components in the 2–150$\mathrm{kHz}$ bandwidth that cause high-frequency signal distortions that can disturb the operation of other supplied loads, including in the field of communication or control. In the case of an increase in the occurrence of supraharmonics, it is necessary to identify the source of the disturbance, taking into account, among others, the indication of its supply point. This article presents the results of observations of supraharmonics in modern power networks. Based on results of long-term research carried out in controlled laboratory conditions and under a real power network in industrial conditions, significant diagnostic problems in the identification of supraharmonic sources related to the influence of typical loads in a low-voltage network are indicated. For the presented cases, the propagation of selected spectral components in a low-voltage network with a branched radial topology is presented. The influence of typical loads in low-voltage networks on the diagnosis of supraharmonics in modern power systems is presented. The possibilities of amplification or supression of supraharmonics by loads that are not their source are demonstrated, depending on their supply point in the power network. Full article

With the evolution of modern power systems, the proportion of renewable energy generation in the grid continues to grow. At the same time, grid operation modes have become increasingly complex and dynamic, leading to heightened uncertainty in disturbance faults. Moreover, power electronic equipment exhibits relatively low-level immunity to disturbances. The issue of frequency stability in power systems is becoming increasingly severe. These factors make the pre-programmed control strategies based on strategy tables, which are widely used as the second line of defense for frequency stability in power systems, prone to mismatches. When a power disturbance occurs, it is crucial to adopt an appropriate emergency load-shedding strategy based on the characteristics of unbalanced power distribution and the network’s frequency profile. In this paper, for a simplified multi-zone equivalent system, the coupling relationship between different load-shedding locations and the system’s frequency response after a disturbance is analyzed. This analysis integrates the power distribution characteristics after the disturbance, a system frequency response (SFR) model, and the frequency distribution law in the network. It is demonstrated that under identical load-shedding amounts and action times, implementing load shedding closer in electrical distance to the disturbance location is more beneficial for stabilizing system frequency. A convolutional neural network (CNN) is employed to localize system faults, and combined with research on the emergency load-shedding amounts based on SFR model parameter identification, a rapid disturbance location-based emergency load-shedding strategy is proposed. This strategy enables prompt and accurate load-shedding actions to enhance the security and stability of the power system. Finally, the effectiveness of the proposed approach is validated using the CEPRI-LF standard arithmetic system. Full article

The growing reliance on mobile robots has resulted in applications where users have limited or no control over operating conditions. These applications require advanced controllers to ensure the system’s performance by dynamically changing its parameters. Nowadays, online bioinspired controller tuning approaches are among the most successful and innovative tools for dealing with uncertainties and disturbances. Nevertheless, these bioinspired approaches present a main limitation in real-world applications due to the extensive computational resources required in their exhaustive search when evaluating the controller tuning of complex dynamics. This paper develops an online bioinspired controller tuning approach leveraging a surrogate modeling strategy for an omnidirectional mobile robot controller. The polynomial response surface method is incorporated as an identification stage to model the system and predict its behavior in the tuning stage of the indirect adaptive approach. The comparative analysis concerns state-of-the-art controller tuning approaches, such as online, offline robust, and offline non-robust approaches, based on bioinspired optimization. The results show that the proposal reduces its computational load by up to $62.85\%$ while maintaining the controller performance regarding the online approach under adverse uncertainties and disturbances. The proposal also increases the controller performance by up to $93\%$ compared to offline tuning approaches. Then, the proposal retains its competitiveness on mobile robot systems under adverse conditions, while other controller tuning approaches drop it. Furthermore, a posterior comparison against another surrogate tuning approach based on Gaussian process regression corroborates the proposal as the best online controller tuning approach by reducing the competitor’s computational load by up to $91.37\%$ while increasing its performance by $63\%$. Hence, the proposed controller tuning approach decreases the execution time to be applied in the evolution of the control system without deteriorating the closed-loop performance. To the best of the authors’ knowledge, this is the first time that such a controller tuning strategy has been tested on an omnidirectional mobile robot. Full article

The proportion of new energy sources, such as wind, photovoltaic and hydropower, in the power grid is increasing year by year. In addition, a large number of nonlinear loads are connected to the grid, resulting in frequent power quality disturbances (PQDs), which pose challenges to the stability and reliability of the power system. Accurate identification of these disturbances is crucial for effective grid management and protection. Although deep learning methods have high accuracy, their lack of interpretability can limit their acceptance in engineering applications. Traditional signal analysis has a good physical foundation, but it is not integrated with deep learning to a sufficient degree. To address these issues, we propose the DWT-1DCNN-LSTM network as an interpretable model for PQD classification. This method effectively decomposes the time-domain signals into sub-signals in different frequency bands by employing the Discrete Wavelet Transform (DWT), which enhances the anti-interference capability of the classification model. This approach enhances the interference resilience of the classification model through the incorporation of the Discrete Wavelet Transform (DWT), which effectively decomposes time-domain signals into sub-signals across different frequency bands. The one-dimensional Convolutional Neural Network (1DCNN) then extracts local features, while the Long Short-Term Memory network (LSTM) analyzes temporal dependencies of the transformed sub-signals. Experimental validation with simulated datasets demonstrates that the DWT-1DCNN-LSTM model achieves an accuracy of 99.27%, outperforming the DWT-1DCNN, 1DCNN-LSTM, LSTM, and CNN models by 1.59%, 1.13%, 1.44%, and 6.48%, respectively. The robustness provided by the DWT module makes the model well suited for PQDs in environments with large disturbances, helping to detect and mitigate PQDs in a timely manner and ultimately contributing to improved power quality and system reliability. Full article

Parameter identification in dynamical systems is a well-known problem with many applications in control design, system monitoring, and fault detection. As these systems are increasingly integrated into complex and demanding environments, challenges such as rapid response, uncertainty handling, and disturbance rejection must be addressed. This paper presents a real-time estimation technique for parameters and load torque in brushless DC (BLDC) motors. These electrical machines are extensively used in engineering applications and often operate under hard conditions. The proposed method is based on algebraic identification, known for its robust performance in both linear and nonlinear systems. In utilizing the mathematical model of a BLDC motor, a set of equations is derived to enable parameter estimation, assuming the availability of input and output measurements in open loop. Moreover, unknown load torque is estimated by approximating the disturbance over a short time window using Taylor series expansion polynomials. The theoretical contribution is analytically validated and is also verified through numerical evaluations revealing the effectiveness of the proposed technique for real-time parameter and disturbance estimation in BLDC motors over other important techniques. Additionally, to address potential peaks in the estimation process, a modification involving an exponent is introduced to mitigate these issues. Full article

In order to improve the control performance of the position sensorless control system of permanent magnet synchronous motors and to reduce the influence of external uncertainties on the control system, such as inertia ingestion and load disturbance, this paper proposes a novel position sensorless control algorithm for permanent magnet synchronous motors based on an interleaved parallel extended sliding mode observer. Firstly, in order to identify the time-varying moment of inertia, load torque and viscous friction coefficient of the system, a novel interleaved parallel extended sliding mode observer based on a single-observer model is proposed, and a robust activator is designed to reduce the coupling between the parameters to be measured. Then, a new predefined-time sliding mode controller is designed for the face-mounted permanent magnet synchronous motor using sliding film control theory, which improves the response speed and control accuracy of the system. Then, the proposed novel interleaved parallel extended sliding mode observer and predefined-time sliding mode controller are used to design the permanent magnet synchronous motor control system, and the stability of the system is proved using the Lyapunov stability theorem. Finally, through simulation analysis and experimental tests, it is verified that the control strategy proposed in this paper can improve the identification accuracy of the motor parameters, reduce the time of identification, and improve the control accuracy and tracking speed. Full article