Power Grid Parameters Research Articles

Nonlinear modeling of measurement errors in gateway energy meters

In order to clarify the quantitative relationship between grid parameters and measurement errors of gateway energy meters, and accurately predict the dynamic measurement errors of gateway energy meters, the author proposes a nonlinear modeling of measurement errors of gateway energy meters. Firstly, elaborate on the NARX prediction model to clarify the basic structure of the nonlinear model; Then propose the process of modeling measurement errors; Finally, through testing, identify the main power grid parameters that affect measurement errors and the optimal structure of the model. The experimental results indicate that: The comparison between the true measurement error of two electricity meters and the measurement error calculated by the nonlinear estimator shows that the Hammerstein Weiner estimator has the highest fitting degree to the true measurement error curve, with fitting degrees of 82.21 % and 85.38 % for the measurement errors of 0.2S and 0.5S electricity meters, respectively. The prediction fit of the NRAX model based on the Hammerstein Weiner nonlinear estimator reaches about 81 % under different load conditions. ConclusionThe model determined by this method can accurately predict the dynamic measurement error of the energy meter, and the research results have positive significance for improving the efficiency of gate energy meter calibration and identifying gate energy meter faults.

Intelligent identification of power grid parameters based on dynamic weighting

Transmission line parameter identification plays a crucial role in power systems. However, existing methods for identifying transmission line parameters face several challenges: (1) when dealing with non-Euclidean data such as power grid data, a considerable amount of noise is introduced, (2) inability to treat the power grid as a global system, limiting calculations to individual transmission lines and overlooking inter-branch correlations, (3) high sensitivity to data contamination. To address these issues, this paper proposes a multi-task noisy graph attention network (MNGAN) for power grid parameter identification, which leverages the spatial structure of the power grid and considers the inter-branch correlations. By computing the homogeneity and average degree of the graph data, we introduce an adaptive attention mechanism to the model. This attention mechanism dynamically adjusts the model’s focus on key information, mitigating the impact of data contamination and improving parameter identification accuracy. Additionally, to achieve simultaneous recognition of multiple branch parameters and enhance model training speed and accuracy, we incorporate a multi-task loss function based on homoscedastic uncertainty. Experimental results demonstrate the superiority of our proposed model over other machine learning and deep learning methods, providing more accurate predictions of branch parameters.

Research on harmonic suppression characteristics and control methods for low-frequency resonance suppressor

The problem of low-frequency harmonics and their resonance seriously threatens the safety of the fusion power supply system, and is one of the key issues that urgently need to be solved during high-power operation of fusion devices. The low-frequency resonance suppressor (LFRS) can compensate for the low frequency harmonic suppression effect of passive filter and active filter and the limitation of voltage and capacity, which is a new approach to solve the low frequency resonance problem of fusion power system. However, the performance of the LFRS is affected by the power grid parameters and the fundamental current circulation between the active filter and passive filter branches. The low frequency harmonic current and its resonance suppression characteristics are analyzed based on the multi-reference vector control strategy in this paper. On this foundation, a current double closed loop control method is proposed, which can ensure the safe and reliable operation of the LFRS system and the suppression effect of low frequency harmonic current. The correctness and effectiveness of the theoretical analysis are verified by RT-LAB Hardware-in-the-loop simulation.

Improvement of real-time state estimation performance in HVDC systems using an adaptive nonlinear observer

This paper proposes a novel adaptive nonlinear observer design for a voltage source converter based on high-voltage direct current (VSC-HVDC) transmission systems. We consider a system consisting of a power grid, and a converter station connected to an unknown load through a long HVDC cable. The primary contribution of this work is the development of a global high-gain observer that facilitates the estimation of all system states. Specifically, it encompasses the estimation of power grid parameters, such as the angular frequency and the voltage at the point of common coupling (PCC), as well as the states of the HVDC cable and the current absorbed by the load. The performance of the proposed observer is assessed through theoretical analysis and simulations. Additionally, we implemented our observer on a digital signal processor (DSP) eZdsp in a processor-in-the-loop (PIL) quasi-real-time setting. Experimental results, coupled with numerical simulations, showcase the outstanding performance of our proposed observer.

Adaptive nonlinear control of the Single-Phase Grid-Connected Photovoltaic System

This paper addresses the problem of controlling the single-phase grid connected to the photovoltaic (PV) system through a three-level boost converter (TLBC) and half-bridge inverter (HBI) with LCL-filter. This study aims to achieve four main objectives: (i) ensure that the output voltage of PV panel track a reference given by the MPPT block based on the perturb and observe (P&O) algorithm; (ii) regulate DC bus voltage to ensure injection of photovoltaic power into the grid; (iii) guarantee power factor correction (PFC); (iv) ensure a balance between TLBC output voltages. The problem is dealt with using a nonlinear controller based on the backstepping and lyapunov approaches, a filtered-PI regulator to ensure the power balance between the grid and the PV panel. An adaptive observer is designed in order to estimate the voltage and the impedance parameters of power grid, by measuring only the grid current. The proposed controller and observer performances are confirmed by simulation in MATLAB\\Simulink.

Multistability of synchronous modes in a multimachine power grid with a common load and their global and non-local stability

The purpose of this work is studying the dynamics of the power grid consisting of an arbitrary number of synchronous generators supplying a common passive linear load. We focus on searching the conditions for the existence and stability of synchronous modes, i.e. the main operating modes of a power grid. The possibility of the existence of non-synchronous (quasi-synchronous and asynchronous) modes is investigated. Methods. To study the dynamics of a power grid we use the effective network model in the form of an ensemble of globally coupled nodes-generators. The state of every node is described by the swing equation. The approach for reducing the effective network to the network with a hub topology (star topology) is proposed. We use numerical methods to construct a partition of the parameter space into areas with different operating modes of the power grid. Results. The conditions for the existence, stability and multistability of synchronous modes are obtained. The main characteristics of these modes are considered, such as the power supplied by generators to the grid and the distribution of currents along transmission lines. We constructed the partition of the power gird parameter space into areas with different dynamics. Conclusion. The power grid consisting of an arbitrary number of synchronous generators supplying a common passive linear load has been studied. We shown the presence of two types of synchronous modes: homogeneous and inhomogeneous. The first is characterized by equal powers and currents flowing through all load supply paths except one. The second provides another additional path, which differs from the others in current and transmitted power. Moreover, the currents flowing along the same path, but in various modes, differ. The presence of high multistability of inhomogeneous synchronous modes has been established. The possibility of coexistence of homogeneous and inhomogeneous synchronous modes, as well as quasi-synchronous and asynchronous modes, is shown. In the power grid parameters space we found areas corresponding both the existence of only synchronous modes and their coexistence with quasi-synchronous and/or asynchronous modes.

Analysis of Power Grid Parameters Depending on the Variable Concentration and Size of Copper Nanoparticles and Aerosol Formation Parameters in the Minimum Quantity Lubrication Method During Turning of Ti6Al4V Titanium Alloy

Analysis of Power Grid Parameters Depending on the Variable Concentration and Size of Copper Nanoparticles and Aerosol Formation Parameters in the Minimum Quantity Lubrication Method During Turning of Ti6Al4V Titanium Alloy

Harmonic analysis on direct-current side of tokamak power supply under asymmetrical triggering

Tokamak is a promising device for using nuclear fusion energy, and the triggering of Tokamak power supply has the characteristics of extreme asymmetry and drastic instantaneous change, which bring spectrum-rich harmonics to the DC side of the power supply, threatening the operational safety of the device. Although the harmonic analysis method of the DC side already exists, however, it still has the problem of cumbersome and inaccurate calculation when it is oriented to fusion power systems with complex operation modes. Based on the three-phase unified switching function, this paper introduces delay factor, and establishes a non-linear function model of converter suitable for the case of asymmetrical triggering. According to the principle of the switching function method, the frequency domain equation of the proposed model is obtained, then combined with power grid parameters, the harmonic analysis of the DC side can be derived. The correctness of the theoretical analysis is verified by comparing the calculation results with the harmonic frequency spectrum data obtained by direct Fourier decomposition of the DC side voltage.

New Tilt Fractional-Order Integral Derivative with Fractional Filter (TFOIDFF) Controller with Artificial Hummingbird Optimizer for LFC in Renewable Energy Power Grids

Recent advancements in renewable generation resources and their vast implementation in power sectors have posed serious challenges regarding their operation, protection, and control. Maintaining operating frequency at its nominal value and reducing tie-line power deviations represent crucial factors for these advancements due to continuous reduction of power system inertia. In this paper, a new modified load frequency controller (LFC) method is proposed based on fractional calculus combinations. The tilt fractional-order integral-derivative with fractional-filter (TFOIDFF) is proposed in this paper for LFC applications. The proposed TFOIDFF controller combines the benefits of tilt, FOPID, and fractional filter regulators. Furthermore, a new application is introduced based on the recently presented artificial hummingbird optimizer algorithm (AHA) for simultaneous optimization of the proposed TFOIDFF parameters in the studied two-area power grids. The contribution of electric vehicle (EVs) is considered in the centralized control strategy using the proposed TFOIDFF controller. The performance of the proposed TFOIDFF controller has been compared with the existing tilt with filter, PID with filter, FOPID with filter and hybrid fractional-order with filter LFCs from the literature. Moreover, the AHA optimizer results are compared with the featured LFC optimization algorithms in the literature. The proposed TFOIDFF and AHA optimizer are validated against renewable energy fluctuations, load stepping, generation/loading uncertainty, and power-grid parameter uncertainty. The AHA optimizer is compared with the widely-used optimizers in the literature, including the PSO, ABC, BOA, and AEO optimizers at the IAE, ISE, ITAE, and ITSE objectives. For instance, the proposed AHA method has a minimized IAE after 34 iterations of 0.03178 compared to 0.03896 with PSO, 0.04548 with AEO, 0.04812 with BOA, and 0.05483 with ABC optimizer. Therefore, fast and better minimization of objective functions are achieved using the proposed AHA method.

Resilience Neural-Network-Based Methodology Applied on Optimized Transmission Systems Restoration

This paper presents an advanced methodology for restoration of the electric power transmission system after its partial or complete failure. This load-optimized restoration is dependent on sectioning of the transmission system based on artificial neural networks. The proposed methodology and the underlying algorithm consider the transmission system operation state just before the fallout and, based on this state, calculate the power grid parameters and suggest the methodology for system restoration for each individual interconnection area. The novel methodology proposes an optimization objective function as a maximum load recovery under a set of constraints. The grid is analyzed using a large amount of data, which results in an adequate number of training data for artificial neural networks. Once the artificial neural network is trained, it provides an almost instantaneous network recovery plan scheme by defining the direct switching order.

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

The energy transition towards resilient and sustainable power plants requires moving from periodic health assessment to condition-based lifetime planning, which in turn, creates new challenges and opportunities for health estimation and prediction. Probabilistic forecasting models are being widely employed to predict the likely evolution of power grid parameters, such as weather prediction models and probabilistic load forecasting models, that precisely impact on the health state of power and energy components. These models synthesize forecasting knowledge and associated uncertainty information, and their integration within asset management practice would improve lifetime estimation under uncertainty through uncertainty-aware probabilistic predictions. Accordingly, this paper presents a probabilistic prognostics method for lifetime planning under uncertainty integrating data-driven probabilistic forecasting models with expert-knowledge based Bayesian filtering methods. The proposed concepts are applied and validated with power transformers operated in two different power generation systems and obtained results confirm that the proposed probabilistic transformer lifetime estimate aids in the decision-making process with informative lifetime distributions and associated confidence intervals.

IMPACTS OF INTEGRATING SOLAR AND WIND PLANTS INTO THE POWER NETWORK OF BHUTAN

Hydropower has been the primary source of electricity in Bhutan, and to achieve power security and sustainability, alternative renewable energy sources (RES) such as solar and wind are being explored. However, the literature reviews present that bus voltage is the most affected parameter during integration. Therefore, this paper presents the impact on the bus voltage due integration of RES into the power network of Bhutan. The measured weather and power grid parameters were used as inputs to the solar and wind farm models developed in MATLAB/Simulink. The output from Simulink was then used as input to the solar and wind model in DIgSILENT Power Factory. The existing Bhutan power grid from 33kV and above has been developed in DIgSILENT to study power flow and results were validated against relevant standards. The voltage profile at the individual bus was maintained at 0.95 to 1.05 p.u. The varying hourly load for 24 hours at the different substations was considered. A quasi-dynamic simulation was performed to study the impact on voltage stability of buses at different penetration levels with every 5% increment. It was observed that at a 25% penetration level, the voltage falls below the accepted limit of 0.95 p.u. The Levelised Cost of Energy for the wind was calculated to be Nu. 13.37/ kWh and Nu. 6.02/ kWh and Nu. 6.51/ kWh from Shingkhar and Yongtru solar, respectively.

Multi-channel information acquisition system based on heterogeneous platform

The acquisition system based on hardware circuit control has more advantages in stability and rapidity than the acquisition system controlled by software. Using heterogeneous platform to complete the acquisition of various parameters of power grid based on hardware circuit is a basic work of smart grid. Therefore, a set of multi-channel data acquisition system based on heterogeneous platform Zynq-7020 is designed to meet the measurement and monitoring requirements of insulation oil temperature and water content in transformer. Firstly, the paper expounds the design idea. Then, taking the heterogeneous platform integrating ARM and FPGA as the core, the system circuit is designed. Furthermore, the design of data acquisition, processing and transmission is completed based on Verilog and C language. Finally, the experimental data of the system are given. The experimental results show that the system has the characteristics of strong expansibility, high precision and strong stability.

Design of Parallel Hybrid Active Power Filter

With the rapid development of social economy, electric energy plays an indispensable role in social life and has become an important symbol of the country’s comprehensive national strength. However, with the rapid development of power electronic technology, many nonlinear loads appear in the power system, which has become the most important harmonic source in the power system. Harmonics will not only affect the normal operation of various electrical equipment, but also damage the peripheral communication system and equipment of public power grid. Therefore, we must control the harmonics in the power grid. The traditional treatment scheme is to use LC passive filter (PF), but it can only filter out harmonics of a specific number of times. In addition,it is sensitive to power grid parameters, may produce resonance, so that the harmonics are amplified, and the volume of the filter device is huge. In order to overcome the above problems, active power filter (APF) appears, which can dynamically compensate harmonics according to harmonic frequency and size, and has excellent flexibility and practicability. However, in terms of cost, the cost of APF with the same capacity is much higher than that of PF. Therefore, based on the respective characteristics of PF and APF, this paper makes an in-depth research and design of hybrid active power filter (HAPF). In this system, the passive filter completes the main harmonic compensation and reactive power improvement, while the active filter is used to help the passive filter overcome the disadvantage of easy resonance.

Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation

Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-informed neural networks (PINN) approach applied to parameter estimation. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN algorithm in estimating the power grid parameters over a wide range of system parameters (including high and low inertia systems) and power grid architectures. Particularly, in case of the slow dynamics system, the proposed SINDy algorithms outperforms the PINN algorithm, which struggles to accurately determine the parameters. Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation. Furthermore, we present an extension of the SINDy algorithm to a scenario where the operator does not have the exact knowledge of the underlying system model. We also present a decentralised implementation of the SINDy algorithm which only requires limited information exchange between the neighbouring nodes of a power grid.

The integration of pelletized agricultural residues into electricity grid: Perspectives from the human, environmental and economic aspects

Agricultural residues are generated during the growing and harvesting of crops; consequently, its availability is linked with the local demand, socioeconomic status, climatic conditions as well as national policies. Only a fraction of these residues has a specific use, mainly in poultry and livestock breeding; however, there is potential to use them as bio-energy feedstock. A strong dependence on oil, as a primary energy source, has resulted in huge environmental problems, particularly climate change. Hence, the use of agricultural residues, particularly those that seem not to have any environmental or economic value; as a feedstock for electricity production, could be an attractive alternative in terms of economic, environmental and social benefits.Therefore, this paper is focused on evaluating these potential benefits from the production of fuel pellets using agricultural residues available from different crops; and their use as a renewable feedstock for electricity production in existing power plants. Life cycle assessment is included in the analysis to get the optimal integration between conventional and non-conventional feedstocks for generating electricity. In addition, the economic aspect was calculated taking under consideration all the activities included in the supply chain for the production of fuel pellets. Also, Human Development Index approach was considered for evaluating the social benefits achieved by the use of renewable fuel pellets. The proposed mathematical model includes all the material balances involved in the supply chain for the production of fuel pellets; also, binary variables were used as decision variables to evaluate the plant installation, giving a mixed integer linear mathematical model optimization. For this case study, parameters of the Mexican electric power grid were used; however, the model can be applied to any other case. The results show that it is possible to provide 200,899 GWh per year of the electricity to cover the demand in Mexico through pellets combustion obtained from agricultural residues. In addition, a carbon dioxide reduction of 20% and 11% increase in social benefit were achieved. Then, the agricultural residues revalorization as solid bio-fuel alternative for the electric power grid has positive economic, social and environmental impacts.

Research on Design, Detection and Realization of Monitoring System Based on Ship Power Grid Parameter

The power system of the ship is closely related to the it’s shipping, production and operation, navigation and communication, daily life and other subsystems, and has a very important impact on the operation of the ship. Due to the characteristics of the micro-grid of the ship and the use of a large number of power electronic equipment, the reliability of the power system of the ship is reduced, and the power quality of the power grid continues to decline. This paper studies the power parameter monitoring of the ship’s power grid, and designs a power parameter monitoring system for the ship’s power grid, which can monitor the power parameters of the ship’s power grid in real time. It is helpful to master the operation of the ship power system, and plays a positive role in improving the power quality of the ship power grid, and ensuring the safety and economic operation of the ship power system.

RETRACTED ARTICLE: Research on subsynchronous oscillation characteristics and suppression strategy of AC network with wind power base

When the wind farm is connected to the power system, certain issues, such as SSO, will be caused, rendering the protection of the power grid unstable. Researchers have found that this situation can be effectively avoided by direct-drive permanent magnet wind power generation. Generates synchronous frequency oscillation is with the help of a grid connection without series compensation. This mode of power generation is part of a new method of subsynchronous oscillation of the wind farm, and the concepts and steps are not explicit. From the point of view of theory, this paper analyzes the causes of its effect, as well as the methods and measures used, and lists the study reports and findings on the synchronous oscillation of grid-connected direct-driven wind farms at home and abroad. At the end of the article, the author also put forward the hypothesis and the imagination of this process. The theory of the wind fire bundling DC transmission system is that the equipment and AC current will communicate, and synchronous electrical oscillation will also occur, and the torsional vibration of the thermal power unit will occur. Our study starts from the investigation of the and explaining the theory behind the wind power and AC network. In combination with the power grid parameter data, the oscillation times of subsynchronous current harmonics in the wind farm are obtained in a specific environment and the conditions of the subsynchronous harmonic generation are analyzed. At the same time, the effect of line harmonic impedance and current oscillation frequency is also simulated.

Design of Coal Mine Power Grid Parameter Monitoring System Based on CAN Bus

In order to ensure the safe production of coal mine enterprises, a real-time power grid parameter monitoring system is proposed by using modern computer and communication technology, so as to ensure the necessary power supply safety for the safe production of coal mines. Based on the analysis of coal mine safety production form and underground special operation environment, this paper introduces can communication technology and ECAN module in detail, designs the overall structure of coal mine power grid parameter monitoring system based on CAN bus, DSP minimum system, can interface circuit and software flow.

Research on Security Level Evaluation Method for Cascading Trips Based on WSN

In recent years, the application of wireless sensor networks (WSN) in power systems has received a great deal of attention. As we all know, the most important issue for the power system is security and stability, especially due to the massive outages caused by cascading trips. Therefore, in today’s era, from the perspective of cascading trips, how to effectively use WSN to analyze and evaluate the security level of the power grid is an important direction for future power development. In this paper, an algorithm based on the WSN collection of online data to calculate the corresponding security level of the system is proposed for the cascading trip phenomenon, to achieve the online evaluation of the cascading trips. First, this paper proposes a hybrid layered network structure based on WSN for monitoring system and details the acquisition of power grid parameters by its acquisition layer. Secondly, combined with the manifestation of cascading trips and the action equation of current‐type line backup protection, the mathematical representation of the grid cascading trips is given, and the mathematical form corresponding to the critical situation is strictly proved, and an index for evaluating the security level of the power grid is proposed and then further combined with the actual physical constraints of the power grid and the establishment of a mathematical model for calculating the security level of the grid cascading trips. For this model, this paper relies on evolution particle swarm optimization (EPSO) to give specific ideas for solving the model. Finally, a case analysis is performed by the IEEE39 node system and the results of the case show the effectiveness of the model and method.