Grid Problems Research Articles

THE IMPORTANCE OF ROOT CAUSES IN SOLVING PROBLEMS IN THE AUTOMOTIVE INDUSTRY

A key element of any quality management system is continuous improvement and a systematic approach to managing non-conformances. The aim of this paper is to use selected statistical methods and quality control techniques to present a way in which an organisation can achieve quality improvement. The 8D methodology was used to solve the observed front grid problem on the bumper of a Porsche Cayenne using a structured eight-step Plan-Do-Check-Act (PDCA) approach to problem solving. As this was an important customer originating from Dubai, it was imperative to immediately start solving the issue that arose with the ACC grid on the front bumper, the problem identified was an existing cut-out in the radar grid. Using the 8D methodology and supporting tools such as the Ishikawa diagram or 5Why, the root cause of the problem was identified and permanent corrective actions were planned and implemented as recommended in the 8D report to reduce the likelihood of the problem recurring and increase customer satisfaction.

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Energy bounds for discontinuous Galerkin spectral element approximations of well-posed overset grid problems for hyperbolic systems

We show that even though the Discontinuous Galerkin Spectral Element Method is stable for hyperbolic boundary-value problems, and the overset domain problem is well-posed in an appropriate norm, the energy of the approximation of the latter is bounded by data only for fixed polynomial order, mesh, and time. In the absence of dissipation, coupling of the overlapping domains is destabilizing by allowing positive eigenvalues in the system to be integrated in time. This coupling can be stabilized in one space dimension by using the upwind numerical flux. To help provide additional dissipation, we introduce a novel penalty method that applies dissipation at arbitrary points within the overlap region and depends only on the difference between the solutions. We present numerical experiments in one space dimension to illustrate the implementation of the well-posed penalty formulation, and show spectral convergence of the approximations when sufficient dissipation is applied.

A bi-level inter-phase coordinated control method for voltage regulation in unbalanced LV distribution networks using PV-battery energy storage systems

Photovoltaic systems coupled with battery energy storage (PV-BES) can help to minimize the effects of variability in PV generation including voltage problems in low voltage distribution grids (LVDNs). Therefore, a joint centralized-decentralized method consisting of two control levels based on sensitivity analysis is proposed in this paper, which employs the BES charging and discharging control in the first level and reactive power capability of inverters in the second control level. When the BES charging and discharging control is insufficient for voltage regulation, the second level of the proposed method is triggered. For full utilization of the BES capacity and to ensure adequate capacity for the following day's voltage regulation needs, an enhancement algorithm is applied to the voltage to active power sensitivity coefficients. All of the voltage sensitivity coefficients are calculated by a central controller using an offline procedure and sent to the PV-BES inverters to determine the amount of active and reactive power in real-time according to the measured local voltage. Taking into account the inter-phase voltage effects, a request for Var compensation signal flow with inter-phase coordination constraints is utilized to properly coordinate the inverters in reactive power control. The proposed method can efficiently regulate voltage by preventing over and under-voltage problems, improving the average total voltage deviation (TVD), reducing the total reactive power absorption and injection by the inverters, and avoiding reduction in PV generation. To evaluate the performance of the proposed control method, it is compared with the commercially available inverter voltage control techniques in an unbalanced residential test feeder. Moreover, a comparison is conducted between the proposed method and an adaptive decentralized control approach presented in the literature. Furthermore, a three-phase Volt-Var strategy, with and without the inter-phase coordination constraints, is simulated that indicates the necessity of the constraints for proper coordination.

Generative neural network models for synthetic solar irradiance sequences

With the significant increase in the amount of solar energy sources, the variation in the production caused by the stochastic nature of these sources becomes a larger problem for the electrical grid. Having a large percentage of installed units, therefore, requires some type of both short- and long-term planning of energy production. Most planning and forecasting tools require a large amount of solar energy data for the location in question and oftentimes that data is difficult to obtain. In this paper, we propose a method for generating synthetic solar irradiance data and test its applicability on forecasting and long-term planning problems. Synthetic data are generated using various generative adversarial networks and masked autoencoder based models, which are all trained on a dataset of solar irradiance data measured on the territory of the city of Banja Luka in Bosnia and Herzegovina. The synthetic data are evaluated both on different types of short-term forecasting tools and a long-term solar plant simulation tool, and the obtained results are compared with the cases where real data is used. By comparing the results, we have concluded that by using generative models we are able to generate synthetic data which is not only realistic, but applicable for both of these uses.

Resilient robust model predictive load frequency control for smart grids with air conditioning loads

AbstractThis paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two‐area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi‐area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances.

An edge‐based event‐triggered delayed distributed algorithm for economic dispatch in smart grids

AbstractThis paper presents an edge‐based event‐triggered delay distributed algorithm for solving the economic dispatch problem (EDP) in smart grids. The objective of the EDP is to minimize the total generation cost by allocating power to individual generators, each with its local generation constraint. To save the overhead of communication resources between agents, an edge‐based event‐triggered mechanism is suggested. By setting distinct triggering thresholds for each communication edge, the agent may efficiently regulate the communication frequency among all neighbors. However, in practice, owing to the instability of the network, the agent may receive information regarding its neighbors, leading to communication lags for every communication link. The virtual agent technique and double‐stochastic matrix augmentation technique provide an equivalent delay‐free EDP. It is demonstrated that the proposed algorithm can asymptotically converge to the global optimal solution as long as the communication delay is arbitrary, time‐varying, and random, but bounded. Objective and clear evidence is provided through a case study in smart grids to verify the algorithm's feasibility.

Distributed Multiagent Reinforcement Learning With Action Networks for Dynamic Economic Dispatch.

A new class of distributed multiagent reinforcement learning (MARL) algorithm suitable for problems with coupling constraints is proposed in this article to address the dynamic economic dispatch problem (DEDP) in smart grids. Specifically, the assumption made commonly in most existing results on the DEDP that the cost functions are known and/or convex is removed in this article. A distributed projection optimization algorithm is designed for the generation units to find the feasible power outputs satisfying the coupling constraints. By using a quadratic function to approximate the state-action value function of each generation unit, the approximate optimal solution of the original DEDP can be obtained by solving a convex optimization problem. Then, each action network utilizes a neural network (NN) to learn the relationship between the total power demand and the optimal power output of each generation unit, such that the algorithm obtains the generalization ability to predict the optimal power output distribution on an unseen total power demand. Furthermore, an improved experience replay mechanism is introduced into the action networks to improve the stability of the training process. Finally, the effectiveness and robustness of the proposed MARL algorithm are verified by simulation.

Practical Test on the Operation of the Three-Phase Induction Motor under Single-Phasing Fault

Single-phasing is a common problem in the three-phase electrical grid. Despite the fact that the fault occurs in one phasing, the three-phase load is affected, and therefore the load is typically turned off. The three-phase induction motor is the most commonly used in the industry; therefore, this research investigates the behavior of the three-phase induction motor under a single-phasing fault. The main aim of this paper is to answer the question, should the three-phase induction motor be turned off under a single-phasing fault? The problem is investigated theoretically and compared with practical tests to explore the parameters of the induction motor (current, stator temperature, and vibration) that are affected under healthy and single-phasing fault conditions. A practical test machine is built to test the motor behavior under single-phasing faults, where the practical experiment results are compared to those of the simulations. Despite the common recommendation under single-phasing fault is to turn off the induction motors, the preliminary results of this study show that turning off an induction motor under single-phasing can be avoided under certain operating conditions with a simple protection scheme, which is useful in some practical situations.

Carbon Neutrality Computational Cost Optimization for Economic Dispatch With Carbon Capture Power Plants in Smart Grid

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.

Fault-tolerant shunt active power filter with synchronous reference frame control and self-tuning filter

This research proposes a novel method that combines Proportional-Integral (PI) control, Artificial Neural Networks (ANNs), and Synchronous Reference Frame (SRF) theory to improve the performance of a three-phase shunt Active Power Filter (APF) under fault situations. The main goal is to reduce power quality problems in electrical grids that are having problems, such harmonics and voltage sags. To precisely manage the APF, the reference frame in which the grid voltages and currents are synchronized is identified using the SRF theory. In order to provide quick and precise correction of voltage and current distortions, the PI controller is integrated to control the APF's compensatory action. The PI controller offers trustworthy control while running normally, but during errors or disruptions, its functionality may suffer. In order to overcome this difficulty, a self-tuning filter in the form of an Artificial Neural Network (ANN) is presented, which may adaptively modify the PI controller's settings under fault situations. To maintain ideal filter performance, the ANN constantly learns from the system's reaction and modifies in real-time. This self-adjusting feature makes sure that even in the event of grid failures, the APF maintains its ability to mitigate problems with power quality. The suggested method effectively reduces harmonics, voltage sags, and other power quality disturbances under both normal and fault circumstances, as shown by the simulation results. In complicated electrical grid systems, the combination of SRF theory, PI control, and ANN-based self-tuning provides a strong way to improve the dependability and effectiveness of three-phase shunt Active Power Filters.

Fully Distributed Economic Dispatch with Random Wind Power Using Parallel and Finite-Step Consensus-Based ADMM

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated with the unpredictability of wind power. The EDP is transformed into an equivalent optimization problem with only an equality constraint and thus can be solved by an alternating-direction method of multipliers (ADMM). Then, to tackle this problem in a distributed manner, the outer-layer framework of the proposed strategy adopts a parallel ADMM, where different variables can be calculated simultaneously. And the inner-layer framework adopts a finite-step consensus algorithm. Convergence to the optimal solution is achieved within a finite number of communication iterations, which depends on the scale of the communication network. In addition, leveraging local and neighbor information, a distributed algorithm is designed to compute the eigenvalues of the Laplacian matrix essential for the finite-step algorithm. Finally, several numerical examples are presented to verify the correctness and effectiveness of the proposed strategy.

Parallel Algorithms for Numerical Solution of Spatially Three-Dimensional Diffusion-Convection Equations in Coastal Systems Based on Splitting Schemes

Introduction. To prevent the occurrence and mitigate the consequences of hazardous and catastrophic phenomena associated with sediment transport in natural systems, it is necessary to develop operational and scientifically justified forecasts, identify critical states at which the emergence of emergency situations is possible. For these purposes, it is necessary to create an accurate and efficient toolkit, including algorithms for numerical solution of a model problem that takes into account the specifics of natural systems. In this work, parallel algorithms for numerical solution of a spatially three-dimensional diffusion-convection problem of sediment are presented, which allow a significant reduction in computation time (by more than 4 times) compared to calculations conducted using a sequential algorithm.Materials and Methods. For the parallel solution of the spatially three-dimensional diffusion-convection problem, an implicit splitting scheme is constructed, in which the original continuous problem is replaced by a chain of two-dimensional and one-dimensional problems. The splitting schemes proposed in the work are physically justified and take into account the specifics of coastal marine systems, for which the influence of micro-turbulent diffusion and advective transport of substances are comparable, and the Peclet number does not exceed unity when approximating real problems. For the parallel numerical implementation, a method of decomposing the grid domain into two families of vertical planes parallel to the coordinate planes Oxz and Oyz, combined with the Seidel method for solving two-dimensional grid problems in horizontal planes and the tridiagonal matrix algorithm when solving one-dimensional three-point problems in the vertical direction, is used. Within the framework of the parallel computing software implementation, a parallel algorithm is presented that implements the diffusion-convection problem on a computing system using MPI technology.Results. A comparative analysis of parallel and sequential algorithms is obtained using a model problem.Discussion and Conclusions. The developed software allows its practical use for solving specific hydrophysical problems, including as part of a software complex.

Estimating Nodal Admittance Matrix for Ill-Posed Inverse Power Flow Problem in Power Grid

Estimating Nodal Admittance Matrix for Ill-Posed Inverse Power Flow Problem in Power Grid

Mitigating supra harmonics in the textile industry using renewable resources: The PCA-WHOGBDT technique for improving grid stability and power quality

Supra harmonics are high-frequency harmonics that can cause a variety of problems in power grids (PGs). This paper proposes a hybrid technique to reduce supra-harmonics in the power grid. The methodology employed in this study combines Principal Component Analysis (PCA) and Wild Horses Optimization (WHO)-based Gradient Boosted Decision Trees (GBDT). The study analyzes network frequency variations, DC voltage development, and voltage noise. It enhances LCCL and LCL filters and evaluates the strategy's effectiveness on weak and stiff grids using real-time data. The proposed PCA-WHOGBDT successfully reduces supra-harmonics to 0.2% of the fundamental in weak grids, outperforming typical levels. It mitigates high-frequency harmonics in both grid types. The PCA-WHOGBDT strategy provides an effective solution for reducing supra-harmonics in hybrid-fed grids, contributing to improved power system quality and stability.

Electric vehicles and the operation of multi-carrier energy systems in the smart grid: a review study

Smart grids have been connected to energy hubs (EHs) with a range of energy carriers and storage capacity to increase the efficiency, flexibility, and dependability of the energy system. One of the most challenging problems in smart grids nowadays is the scheduling of EHs. Although the interaction of several devices, such as EHs and electric vehicle charging stations (EVCSs), can help improve their performance, developing such intricately interwoven systems is difficult. In this paper, with the help of several papers, the impact of EVCSs on the optimal operation of EHs is investigated, despite the uncertainty surrounding electric vehicle charging behaviours. Additionally, the formulation concerning the energy hub's component parts, electric vehicles (EVs) in a single hub, and interconnected hubs is investigated. In addition, some simulation results from reviewed papers are presented and analyzed in this paper. The main goal of this paper is to analyze the effects of EVCS on the optimal management of EHs. Furthermore, some models of energy hubs integrated by EVCS are presented, and a numerical analysis is conducted to examine the function of EVCS in conjunction with electrical and thermal storage systems.

Turbomachinery GPU Accelerated CFD: An Insight into Performance

Driven by the emergence of Graphics Processing Units (GPUs), the solution of increasingly large and intricate numerical problems has become feasible. Yet, the integration of GPUs into Computational Fluid Dynamics (CFD) codes still presents a significant challenge. This study undertakes an evaluation of the computational performance of GPUs for CFD applications. Two Compute Unified Device Architecture (CUDA)-based implementations within the Open Field Operation and Manipulation (OpenFOAM) environment were employed for the numerical solution of a 3D Kaplan turbine draft tube workbench. A series of tests were conducted to assess the fixed-size grid problem speedup in accordance with Amdahl’s Law. Additionally, tests were performed to identify the optimal configuration utilizing various linear solvers, preconditioners, and smoothers, along with an analysis of memory usage.

Circle formation by asynchronous opaque fat robots on an infinite grid

This study addresses the problem of ‘Circle Formation on an Infinite Grid by Fat Robots’ ( CF _FAT _GRID ). Unlike prior work focused solely on point robots in discrete domain, it introduces fat robots to circle formation on an infinite grid, aligning with practicality as even small robots inherently possess dimensions. The algorithm, named CIRCLE _FG , resolves the CF _FAT _GRID problem using a swarm of fat luminous robots. Operating under an asynchronous scheduler, it achieves this with five distinct colors and by leveraging one-axis agreement among the robots.

Low carbon integrated vehicles and buildings

This paper defines vehicles and buildings as main sources of United Kingdom (UK) carbon dioxide (CO2) and seeks to cut such emissions using green hydrogen made from combined wind and solar energy. Combustion vehicles powered by fossil petroleum emit near half of UK climate-warming CO2 while buildings heated by natural gas provide a third. First, current UK grid problems are defined: Electricity, gas and petroleum grids. Refueling green vehicles has been a particular problem. Then experiments on the private wire community of Keele University show how green hydrogen could integrate both green vehicles and buildings. Next, the model supply chain is planned and tested. Finally, experiments and calculations are outlined, analyzing the optimum system design criteria proposed. We conclude that economic green hydrogen can displace petroleum in vehicles, while powering buildings instead of natural gas. Also, the prospect in 2024 is that profits can be made all along the green hydrogen supply chain, such that new businesses involved in local private clean communities can cost less than the National Grid monopoly and other dominant fossil energy companies.

Distributed Online Constrained Optimization With Feedback Delays.

We investigate multiagent distributed online constrained convex optimization problems with feedback delays, where agents make sequential decisions before being aware of the cost and constraint functions. The main purpose of the distributed online constrained convex optimization problem is to cooperatively minimize the sum of time-varying local cost functions subject to time-varying coupled inequality constraints. The feedback information of the distributed online optimization problem is revealed to agents with time delays, which is common in practice. Every node in the system can interact with neighbors through a time-varying sequence of directed communication topologies, which is uniformly strongly connected. The distributed online primal-dual bandit push-sum algorithm that generates primal and dual variables with delayed feedback is used for the presented problem. Expected regret and expected constraint violation are proposed for measuring the performance of the algorithm, and both of them are shown to be sublinear with respect to the total iteration span T in this article. In the end, the optimization problem for the power grid is simulated to justify the proposed theoretical results.