Digital Simulation Platform Research Articles

DC fault current clearance coordinated control strategy for DC grid with hybrid MMC

For DC grid based on the hybrid modular multilevel converter (MMC), the traditional DC fault current clearance scheme takes a long time and greatly affects the active power transmission. A novel DC fault current clearance coordinated control strategy is proposed, which can quickly interrupt the fault current and reduce the impact of DC fault on the DC grid and AC system. For fault-line connected MMC (FLMMC), a negative DC voltage control is employed, which can improve the current attenuation speed and ensure reliable fault isolation. For non-fault-line connected MMC (NFLMMC), the active current-limiting control (ACLC) based on the virtual reactor is adopted, which can reduce the current flow to the fault location and further shorten fault isolation time. To reduce the impact on the AC system during the DC fault, a short-time active power support control is designed. Finally, a four-terminal DC grid simulation model is built based on the RT-LAB OP5600 real-time digital simulation platform. The simulation results show that the proposed coordinated control strategy for the DC grid can quickly clear DC fault current, shorten DC fault isolation time, and strengthen active power support capability.

Power Flow Control Strategy of Wind Power Hybrid Power Grid Based on Digital Simulation Cloud Platform Architecture

Wind power hybrid is a comprehensive utilization method of new energy, which can greatly reduce energy pollution, but there is also the problem of tidal flow difference, so the power flow regulation of wind power energy is to be solved at present. In this paper, the digital simulation platform is used to comprehensively judge the grid-connected demand, collect data such as wind speed, power generation and energy storage, and regulate the power flow of the wind power hybrid grid in combination with the problem of wind speed randomness. Secondly, the intelligent control function is used to judge the wind turbine transmission power hybrid grid and the power flow of the distributed power grid to balance the stability of the overall power flow, voltage and current of the wind power hybrid power grid. The results show that the digital simulation cloud platform collects the power flow data of the wind power hybrid power grid, generates abnormal power flow points, and supplements the power flow through alternative methods, and its regulation and control effectiveness is greater than 90%, and the amplitude of maintaining the power flow is between 10~20%. The wind power grid's grid-connected power flow regulation time is within 10s, and there is no damage to the wind turbine and energy storage battery. Therefore, the digital simulation cloud platform can realize the power flow control of the wind power hybrid power grid, maintain its overall stability, and meet the needs of new energy grid expansion.

Distributed chaotic bat algorithm for sensor fault diagnosis in AHUs based on a decentralized structure

The effective operation of sensors in heating, ventilation and air conditioning (HVAC) systems to promote high-efficiency, energy-saving and low-risk intelligent buildings. However, most existing methods are either based on a centralized architecture or employ only pure digital simulation platforms for HVAC sensor fault diagnosis and verification, which are difficult to meet the complex and dynamic needs of HVCA systems. To overcome the difficulty in employment of centralized architecture involved multi sensor fault diagnosis and fault diagnosis methods limited by pure digital simulation platform, a fully distributed chaotic bat algorithm is proposed to diagnosis multiple-sensor faults and is verified by the hardware-in-the-loop simulation platform in HVAC systems. First, inspired by the bat swarm intelligence and multiple agents, we characterize the sensor fault diagnosis problem as an optimization problem of the bat predation behavior. Specifically, each sensor node is abstracted as a bat colony, and each bat colony communicates with directly connected neighboring bat colony in physical space. Second, to avoid the tendency of bat algorithm to fall into locally optimal solutions, the tent chaotic map is employed to enhance the bat colony search ability for improving the randomness of bat algorithm, involves escaping from local extreme points and increasing the diversity of the bat colony. Finally, we embed the temperature and humidity sensors into the hardware-in-the-loop simulation platform to construct the near-actual physical environment. The performance and convergence of the proposed method are verified using both a pure digital simulation platform and a built-in hardware-in-the-loop simulation platform. Compared with basic bat algorithm (BA), particle swarm optimization algorithm (PSO) and chaotic bat algorithm (CBA), the proposed fully distributed chaotic bat algorithm (DCBA) have higher accuracy and can achieve the 98.41 % accuracy in fixed bias (FB) sensor faults, 96.96 % accuracy in complete failure (CF), 95.84 % in drift bias (DB) and 96.08 % accuracy in accuracy drops (AD).

Error analysis and improved method of time-domain distance protection for wind power transmission lines

Time-domain distance protection has a good transient performance and operation speed when applied to wind power transmission lines. In this paper, the error analysis and improved method of phase-to-phase time-domain distance protection are investigated. Firstly, based on the time-domain mathematical model, the location error caused by fault resistance is analyzed, and then the algorithmic convergence and accuracy of conventional phase-to-phase distance relay are evaluated. To solve the computational non-convergence problem, an improved phase-to-phase distance relay is proposed, in which the instantaneous negative-sequence current is used to improve the location accuracy, especially during the fault transient stage of wind farms. In order to avoid the protection overreach problem, the threshold selection method is designed based on the maximum allowable error of the calculated distance. According to the proposed method, the corresponding protection device is developed. And the hardware-in-the-loop (HIL) test system is built based on the real-time digital simulation (RTDS) platform. Extensive experimental tests are carried out, to verify the feasibility and superiority of the proposed time-domain distance protection.

Validation and Implementation in the Cloud of Cell-Level Models of a Digital Twin Simulation Platform

The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. These Li-Ion Batteries can consist of thousands of cells with series/parallel connections, which suffer from operating and degradation deviations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. To solve this problem, this paper proposes a Digital Twin Simulation Platform that considers all the individual cells based on the Cloud to extend the computational power and data storage capacity. This work presents validated cell models, a module-level modelling approach, and an experimental validation platform is suggested. In addition, the first results obtained when implementing the Digital Twin Simulation Platform in the Cloud are presented.

CrowdBot: An Open-Environment Robot Management System for On-Campus Services

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these limitations, we introduce CrowdBot, a robot management system that enhances service in campus environments. Our system leverages a hierarchical reinforcement learning-based cloud-edge hybrid scheduling framework (REDIS), for efficient online streaming task assignment and dynamic action scheduling. To verify the REDIS framework, we have developed a digital twin simulation platform, which integrates large language models and hot-swapping technology. This facilitates seamless human-robot interaction, efficient task allocation, and cost-effective execution through the reuse of robot equipment. Our comprehensive simulations corroborate the system's remarkable efficacy, demonstrating significant improvements with a 24.46% reduction in task completion times, a 9.37% decrease in travel distances, and up to a 3% savings in power usage. Additionally, the system achieves a 7.95% increase in the number of tasks completed and a 9.49% reduction in response time. Real-world case studies further affirm CrowdBot's capability to adeptly execute tasks and judiciously recycle resources, thereby offering a smart and viable solution for the streamlined management of campus services.

Stability assessment of inverter‐based renewable energy sources integrated to weak grids

AbstractThe worldwide electricity network is undergoing a crucial transformation, shifting from traditional synchronous generators to inverter‐based renewable energy sources (IRESs). This shift is expected to reduce the grid's available fault level (AFL), potentially impacting grid functionality, particularly during the integration of IRESs into weak grids. This paper examines the challenges associated with weak grids, focusing on the steady‐state and dynamic stability of IRES when integrated into these systems. In the steady‐state analysis, the effects of AFL, injected power volume, and grid characteristics on the steady‐state voltage at the point of interconnection were explored. For dynamic stability, eigenvalue and H2 norm analyses are used for evaluation. Sensitivity analysis is conducted to assess the impact of these factors on the stability of IRESs connected to weak grids. A detailed case study using the IEEE 39 bus test power system is included to demonstrate our findings, where the steady‐state and dynamic stability of IRES connected to the test system are assessed using the proposed methods. The accuracy of these analyses is confirmed by extensive simulation studies on the OPAL‐RT real‐time digital simulator platform.

Coordinated Control of Intentional Islanding and Phase Sequence Exchange for Enhancing Transient Stability of Isolated Power Grid

An isolated power grid (IPG) is electrically weakly connected to the utility power grid, and intentional controlled islanding (ICI) can secure IPG from external faults. Phase Sequence Exchange (PSE) is a recently developed emergency control technology in which power electronic devices are used to switch the three-phase sequence, thereby reducing the power angle of the generator by 120° and preventing the system from losing stability. This paper proposed a coordinated control scheme for PSE and ICI. After the system is disturbed, the local measurement of the interconnection line between IPG and the utility grid is used to assess the system's stability. PSE is adopted to adjust the power flow direction to reduce the transient energy of IPG. At the same time, using the internal measurement of IPG, the transient energy of IPG is calculated in real-time, and the critical energy is rolling refreshed based on offline information. After PSE reduces the energy of IPG to lower than the critical energy, IPG is safely disconnected from the utility grid. The proposed scheme needless to adjust the internal generator or load of IPG and is verified on the real-time digital simulator (RTDS) platform with a PSE prototype.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

A robust damping control for battery energy storage integrated power systems to mitigate inter-area oscillations

This paper presents the effect of a Battery Energy Storage System (BESS) on the power system inter-area oscillations under changing load conditions. The dynamic interaction between the BESS control modes and synchronous generators, along with BESS control parameters, influence the damping of prominent inter-area oscillations under different loading conditions. Furthermore, H∞ mixed sensitivity scheme-based Wide-Area Damping Controller (WADC) is proposed for BESS. This controller is designed using the Linear Matrix Inequality (LMI) framework to mitigate the prominent inter-area oscillation of the power system. Sensitivity to control parameter variation and the geometric measure of the observability are utilized to select the location and feedback signal for the WADC. The performance of the proposed WADC is compared with an Energy Storage System-based stabilizer and WADC proposed in an existing work on a modified New England ten-machine benchmark system. The Real-Time Digital Simulator (RTDS) platform is used to assess the real-time viability of the proposed WADC. The simulation results demonstrate that the proposed WADC can effectively diminish several inter-area modes. Additionally, it offers robust performance under varying load conditions, contingencies and with communication delays in feedback signals. The proposed controller is also effective under the system integrated with Doubly Fed Induction Generator-based Wind Turbine System.

Robust damping control for integrated wind turbine power networks during low inertia condition

AbstractThis paper presents a modal analysis‐based impact analysis of the change in system inertia due to the integration of the large‐scale doubly fed induction generator (DFIG)‐based wind turbine system (WTS) on the power system damping in inter‐area oscillation modes. The impact of replacement of a synchronous generator by WTS of same MVA capacity on system dynamics such as emergence of new critical modes of inter‐area oscillations affecting system stability is studied. Further, a double‐channel scheme‐based wide‐area damping controller (WADC) is proposed for WTS, which provides sufficient damping of prominent oscillation modes and enhanced stability margin to alleviate the negative impact of reduction in system inertia. The comparative assessment of the proposed controller with a PSS‐based WADC on a modified New England 10‐machine system validates the performance and is found to be more effective in mitigating inter‐area oscillation in the system. The real‐time feasibility of the proposed double‐channel WADC is evaluated on the Real‐Time Digital Simulator (RTDS) platform. The simulation results show that the proposed WADC can successfully mitigate various inter‐area modes simultaneously. Furthermore, it provides robust performance for a variety of contingencies, time delays in feedback signals, and uncertainties associated with different operating scenarios of WTS.

Review of Synchronization Algorithms used in Grid-Connected Renewable Agents

This paper presents a review of several synchronization algorithms used in Grid-Connected Renewable Agents. Different synchronization algorithm structures will be shown and their advantages and disadvantages will be discussed using MATLAB/SIMULINK tool from The MathWorks, Inc. Some simulations will be shown firstly, and secondly, a Real Time Digital Simulator (RTDS) platform will be used for testing and debugging the Synchronization Algorithms.

A novel power control scheme for distributed DFIG based on cooperation of hybrid energy storage system and grid-side converter

Due to the uncertainty of wind energy, the wind power is difficult to be dispatched and may cause the voltage fluctuations for distributed network. Therefore, a novel power control scheme is proposed to manage the output power of the distributed doubly-fed induction generator (DFIG) by cooperating the hybrid energy storage system (HESS). In the proposed scheme, the grid-side converter of DFIG is controlled to manage overall output power of the DFIG/HESS hybrid system in accordance to the power dispatching command. The HESS is utilized to deal with the power difference between DFIG generation power and the power dispatching command. The HESS is embedded in the DC-link bus of DFIG and is composed of superconducting magnetic energy storage and batteries. Additionally, in order to avoid HESS from overcharging and over-discharging, the pitch angle control and power dispatching command are adjusted by considering the state of charge (SOC) of HESS. Finally, the simulation model of distributed DFIG and HESS is built on the real-time digital simulation (RTDS) platform, and the simulation results validate the effectiveness and reliability of the proposed power control scheme.

Theoretical Development and Experimental Evaluation of a Nonlinear Observer for sensorless WECS

This research paper introduces a novel sensorless wind energy conversion system (WECS) with a Vienna rectifiers-based high-gain observer. The main emphasis is on crafting a high-gain observer capable of providing real-time estimates for challenging-to-measure signals like rotor speed and load torque. Addressing the complexities arising from system nonlinearity and high dimensionality, the paper showcases the effectiveness of the proposed observer through simulations and theoretical analysis. Furthermore, an experimental validation on the RealTime Digital Simulator (RTDS) platform validates the observer’s performance and reliability, highlighting its potential for enhancing sensorless WECS technology.

Digital Technology-Driven ESG Simulation Platform and Sustainable Yield Assessment for Investors: Case Study in China

Digital Technology-Driven ESG Simulation Platform and Sustainable Yield Assessment for Investors: Case Study in China

Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction

As a vital infrastructure in modern cities, district heating (DH) systems provide stable high-quality heat sources. To satisfy the demands of energy-saving, emission-reduction policies, and user preferences, a high-precision digital simulation platform is essential for district heating network (DHN) scheduling. In this paper, a method for digital twin (DT) modeling based on hydraulic resistance identification using an online adaptive particle swarm optimization (APSO) algorithm is proposed. Additionally, Singular Spectrum Analysis and back propagation artificial neural network algorithms (SSA-BP) are proposed to achieve heat load prediction in order to provide sufficient conditions for DHN scheduling. The load prediction for the substation is completed by predicting the secondary return water temperature at the subsequent time step based on the historical outdoor temperature time series and secondary supply water temperature of the substation. Accordingly, simulation analysis has been conducted on its application to an existing DHN in Tianjin. With the proposed methods, the simulation errors are reduced for about 80 % substations in the case DHN compared to mechanistic models when simulating hydraulic conditions. Among these, 77.49 % of substations were reduced by 0 %–3 %, and 1.43 % of substations were reduced by 3 %–5 %. The prediction errors of secondary return water temperature for case substations A and B exhibit random variation trends, with maximum absolute errors within the range of 0.02 °C and 0.06 °C, respectively.

Analytical voltage sensitivity-based distributed Volt/Var control for mitigating voltage-violations in low-voltage distribution networks

Integrating solar photovoltaic system in low-voltage distribution networks leads to significant voltage violations. This issue can be alleviated using the cutting-edge control techniques (Volt/Var control, Volt/Watt control, etc.) of smart inverters. Moreover, the smart inverters absorb/inject the necessary amount of reactive power from/into the substation to alleviate the over/under voltages. This increases the reactive power burden on the substation and deteriorates its power factor. Therefore, this paper proposes a novel distributed Volt/Var control technique to reduce the dependency on substations for reactive power support and improve its power factor. The proposed technique is based on analytical voltage sensitivity analysis. By developing suitable relations, the feeder nodes are categorized into two groups, one operating in lagging mode, while the other in leading mode. This ensures proper utilization of reactive power capability of the inverters. An IEEE 13-node low voltage distribution network with the solar photovoltaic system at different load conditions is used to test the proposed and other existing Volt/Var control techniques. Compared to other distributed Volt/Var control techniques, the proposed method requires a minimum reactive power support to regulate the feeder voltage. Additionally, the obtained results by the proposed method are closer to those by the optimal reactive power support approach using GAMS software and are validated on a real-time digital simulator platform.

Spatiotemporal Pattern Analysis of Nodal Inertia Using Convex Combination of Walks of Generator Nodes in Varying Inertia System

The distinct requirement of high control and faster frequency regulation from wind turbine generator (WTG) needs to look at the network's local and global vulnerabilities. In line with the above objective, the article elucidates 1) The role of generation units' contribution in handling the disturbances and their propagation in the network, and 2)The behavior of dynamic response of the nodes in the presence of faster frequency regulation action from WTG. The authors propose a novel online Nodal Inertia Index (NII) that considers the network's structural features and generator dynamic response in the varying inertia system. NII investigates the inherent property of the distribution of inertia in the system by the concept of graph theory convex combination of walks of generator nodes. Further, the article explores the dynamic response of nodes (DRN) in terms of disturbance location from the generator bus and also considers generators' internal interactions. DRN employs the Fiedler vector to observe the global response and locational impact. The application of NII and DRN in selecting reserve percentages of the WTG is showcased. All the analyses are performed in the modified IEEE 9,39 and 68 bus, coordinating between the Real-Time Digital Simulator (RTDS) and MATLAB platform.

A novel decentralized inverter control algorithm for loss minimization and LVRT improvement

Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters’ fault-ride-through capability. This can become crucial for the reliable operation of the distribution grids, as they could lose valuable resources to support grid voltage at the time they need them the most. This paper explores how two novel loss-minimizing algorithms can both achieve high reduction of the system losses during normal operation and remain connected to support the voltage during faults. The algorithms we propose are decentralized and model-free: they require no communication and no knowledge of the grid topology or the grid location of the converters. Using local information, they control the reactive power injection to minimize the system losses. In this paper, we extend these algorithms to ensure the low voltage ride through (LVRT) capability of the converters, and we integrate them with state-of-the-art Wavelet-CNN–LSTM RES forecasting methods that enhance their performance. We perform extensive simulations on the real-time digital simulation (RTDS) platform, where we demonstrate that the algorithms we propose can achieve a substantial decrease in power losses while remaining compliant with the grid codes for LVRT makes them suitable for the implementation across the distribution system.

A non-unit line protection method for MMC-HVDC grids based on the curvatures of backward traveling waves

The existing protection techniques for high-voltage direct-current (HVDC) grids suffer from several shortcomings such as high sampling frequency, poor robustness, and reliance on simulation for threshold setting. To solve these problems, this paper proposes a non-unit protection method for modular multilevel converter (MMC)-based HVDC grids using the curvatures of backward traveling waves. To this end, the propagation characteristics of traveling waves and the boundary characteristics of DC lines are first studied, then the analytical expressions of backward traveling waves are derived. Moreover, the curvatures of backward traveling waves are analyzed. On this basis, a non-unit protection method is proposed, including zone selection, disturbance identification, and pole selection. At last, with a protection platform and a real-time digital simulator (RTDS) platform of the MMC-HVDC grid, the accuracy and the robustness of the proposed protection method are verified. The results show that the protection method can correctly identify faults with different distances and resistance in 1 ms and has strong robustness against transition resistance, sampling frequency, boundary value, noise, system topology, and line parameters.