Power Grid Analysis Research Articles

Power system Frequency Prediction after disturbance based on Deep Learning

In order to ensure the safe and stable operation of power system, enrich the means of power grid analysis and control, and expand the application of deep intelligent learning methods in power grid systems, the application of deep learning intelligent machine learning method in frequency prediction of large power grid is explored. First, on the basis of deep learning, the frequency response mode of large power grid is analyzed and the key characteristic quantities that affect the frequency response mode are extracted. Second, the deep belief neural network (DBN. DNN) frequency prediction model is constructed. Also, the training and testing of the model are introduced. Finally, the input and output based on the DBN.CNN prediction model and the network structure design of the model are analyzed. The prediction performance of the model is evaluated. The results show that when the number of neurons in the hidden layer is 50, the model achieves the optimal prediction effect. Increasing the number of training samples helps to improve the modeling ability and prediction accuracy of the model. For frequency prediction problems, the number of training samples should be set to ≥400, and the number of hidden layers corresponding to the model should be 5. When the number of hidden layer neurons is 10, the prediction accuracy of the DBN/DNN network is poor. When the number of hidden layer neurons is 50, the model can achieve the best prediction effect. Overall, the DBN.DNN prediction model has good prediction performance. The RMSE of the forecast data is O. 0073Hz can basically meet the actual application requirements. Therefore, the frequency prediction method based on deep belief neural network has certain advantages in accuracy and efficiency.

Research on the Method of Improving the Quality of Electric Energy Measurement Data

In the process of collecting data in the power grid harmonic monitoring and analysis system, changes in load, operating modes, and equipment failures will cause data changes. For the strong correlation index, this paper establishes a mathematical model of the monitoring value for the measurement index, while for the weak correlation index, considering that the monitoring data is a type of time series data, a time series model of the monitoring value for the measurement index can be established, and then the abnormal value monitoring is performed by statistical methods. Moreover, experiments prove that the research method used in the paper to detect whether abnormal data exists in the data can effectively improve the efficiency and provide a guarantee for the quality of data monitoring.

Bounding Regression Errors in Data-Driven Power Grid Steady-State Models

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios drawn from an underlying distribution, and proposes an evaluation implementation based on Rademacher complexity theory. We answer critical questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Different from traditional Rademacher complexity that mainly addresses classification problems, our method focuses on regression problems and can provide a tighter bound. Our results are crucial for the evaluation and application of data-driven models in power grid analysis. We demonstrate the proposed method by finding generalization error bounds for two applications, i.e., branch flow linearization and external network equivalent under different degrees of physical knowledge. Results identify how the bounds decrease with additional power grid physical knowledge or more training data.

Multi-period vulnerability analysis of power grids under multiple outages: An AC-based bilevel optimization approach

This paper describes a methodology for the N-k contingency analysis of bulk power systems. The method encompasses the evaluation of contingencies’ effects on the power system over a range of system demand levels. The proposed model is inherently a multi-period bilevel optimization problem. Unlike the conventional bilevel optimization problems for the N-k contingency analysis, the proposed model considers the effects of reactive power dispatch, losses, and voltage profile. In doing so, the problem is formulated as a multi-period AC-based bilevel mixed-integer nonlinear programming (MINLP) problem. To guarantee the global optimality of the solution, this paper linearizes and then transforms it into a one-level mixed-integer linear programming (MILP) problem using different linearization techniques and the duality theory. The simulation results on the annual load profile of the IEEE Reliability Test System (RTS) verify the effectiveness of the proposed model.

Research and application of a real-time operation risk assessment method for power grid based on random forest algorithm

Real-time operation risk assessment of power grid is not only an important content of power grid dispatching and operation control, but also an important reference factor in power grid accident disposal. It is difficult for the traditional evaluation methods to meet power system online operation control requirements, because of their complex process and long calculation time. To this end, a real-time power grid operation risk assessment method based on random forest intelligent algorithm is proposed in this paper, The basic concepts and implementation points of random forest algorithm is introduced. The implementation framework of real-time operation risk assessment based on random forest is proposed taking the need of power grid operation risk assessment into consideration. Compared with the traditional evaluation method, this method can calculate the power grid operation risk level directly according to some key operation indexes without complex power grid analysis, such as N-1 security check. Finally, the two-years practical application in a certain provincial power grid in China shows that the method has high evaluation accuracy and computational efficiency, which can match the traditional evaluation method and improve the calculation efficiency and accuracy of risk assessment.

Enhanced harmonic state estimation in unbalanced three-phase electrical grids based on the Kalman filter and physical scale-down implementation

A time-domain methodology is proposed based on Kalman filter, to determine the harmonic state estimation in three-phase electrical grids. Time-domain methodologies based on the Kalman filter need an initial solution to begin estimating the harmonic state in electrical grids. Previous methods have found this solution using simulated electrical grids; thus, the measurements taken from the simulations are synchronised only with the simulated grid model. In practical power networks, measurements taken from the grid need to be synchronised with the theoretical grid model. Synchronization between the model and measurements using the Kalman filter is proposed. This paper also proposes an alternative method to obtain the periodic steady-state solution in power grids. The method adequately processes the information given by a limited number of measurements to find the required solution. An experimental case study for a three-phase unbalanced condition with a nonlinear load and a limited number of measurements is used to validate the proposed methodology, i.e. practical data has been used instead of synthetic data. Hence, the results are validated by direct comparison of the state estimation response against the actual data recorded from the experimental laboratory implementation. The results show that using the proposed methodology, the time-domain harmonic state estimation can be efficiently applied to the analysis of practical power grids.

ATC assessment and enhancement of integrated transmission and distribution system considering the impact of active distribution network

Assessment and enhancement of transmission network capability have been one of the prime interests of power system monitoring, operation and control. Conventionally, the issue of available transfer capability (ATC) assessment has been tackled considering the transmission and distribution networks as segregated entities because they had little or no influence on each other's performance owing to the unidirectional flow of power from the transmission to distribution networks. Now, with increasing interest in smart transmission and distribution in amalgamation with a paradigm shift in sources of generation from centralised to decentralised even at distribution levels mandating the analysis of power grid while inculcating both the hierarchies simultaneously. In this study, a platform for quasi-static operation of power transmission and distribution networks, employing a multi-agent based system has been developed. The developed multi-agent systembased system has been utilised for assessment of ATC considering active distribution network (ADN) whereby Modified IEEE 24 bus system at transmission and Modified IEEE 123 node system at distribution level has been considered as a test system. It has been observed that the presence of ADN considerably affects the ATC of the system.

Stable operation analysis of a low voltage power grid with power injection of photovoltaic systems

Este trabajo presenta el estudio de una red eléctrica de baja tensión en estado estable con inyección de potencia de sistemas fotovoltaicos (SFV), con el propósito de cuantificar el impacto a partir de indicadores asociados al valor eficaz, perfil de tensión, desbalance, pérdidas en los conductores y sentido de flujos de potencia. La red modelada fue estudiada para cinco escenarios de ubicación de SFV y diez porcentajes de inyección de potencia FV (1%, 3%, 5%, 10%, 15%, 20%, 25%, 50%, 75% y 100% de la capacidad nominal del transformador). Se realizó un análisis cuasi dinámico con pasos de una hora durante un año. Los resultados indican que el impacto producido en los parámetros eléctricos analizados depende del lugar y de la capacidad instalada de los autogeneradores FV; los porcentajes de inyección superiores al 50% son desfavorables para esta red en los cinco escenarios

Effective sensitivity-based method for N-1 contingency analysis of VSC-based MTDC power grids considering power generation droop speed controls

This paper introduces a sensitivity-based approach for the efficient calculation of the post-disturbance, steady-state conditions in VSC-based MTDC power grids subject to generation outages and VSC disconnections. This approach is useful for the N-1 contingency analysis, a crucial tool in power system control centres. In order to maintain a high degree of fidelity, the approach considers state-of-the-art VSC control strategies: converters with DC voltage regulation, power-injection control, DC voltage/power droop control, and converters imposing frequency regulation on passive grids. The speed-governing response of power plants is also considered, enabling accurate calculations of AC/DC power flows and AC system frequencies. Indeed, the derived sensitivity factors permit the primary frequency response of the AC systems and DC grid to be efficiently assessed, something that would require several hours of simulation by standard electromagnetic transient (EMT) simulators. Evidently, this novel approach allows a much faster calculation of the AC/DC network’s post-disturbance conditions than is possible with dynamic simulation tools. A formulation with such modelling versatility and practicality does not currently exist elsewhere.The usefulness of this novel formulation has been confirmed using an MTDC system comprising six VSC-connected AC grids and a 13-bus DC grid. The disconnections of VSC units and power plants have also been evaluated. The study shows that the post-disturbance, steady-state conditions computed by the sensitivity-based approach agree well with those obtained by full dynamic simulations, because the relative errors between the two fundamentally different methods are smaller than 0.1% for the AC system frequencies and smaller than 4% for the AC and DC transmission line power flows.

Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey

Understanding and analyzing cascading failures in power grids have been the focus of many researchers for years. However, the complex interactions among the large number of components in these systems and their contributions to cascading failures are not yet completely understood. Therefore, various techniques have been developed and used to model and analyze the underlying interactions among the components of the power grid with respect to cascading failures. Such methods are important to reveal the essential information that may not be readily available from power system physical models and topologies. In general, the influences and interactions among the components of the system may occur both locally and at distance due to the physics of electricity governing the power flow dynamics as well as other functional and cyber dependencies among the components of the system. To infer and capture such interactions, data-driven approaches or techniques based on the physics of electricity have been used to develop graph-based models of interactions among the components of the power grid. In this survey, various methods of developing interaction graphs as well as studies on the reliability and cascading failure analysis of power grids using these graphs have been reviewed.

Research on Distribution Network Loss Allocation with Small Hydropower Groups

On the basis of Longnan power grid with small hydropower groups, this paper proposes a loss allocation method combining marginal loss coefficient method and branch dissipation power transfer component method, aiming at the fact that marginal loss coefficient method does not consider the influence of balancing nodes and different balancing nodes on allocation results. Firstly, starting from the circuit law, Norton’s equivalent is used to equivalent all power supplies to node injection current. Based on the branch dissipation power transfer component method, the network losses that each power supply node including the balancing node should share are obtained. Then, the marginal network loss coefficient method is used to obtain the network losses shared by other nodes except the balancing node. Finally, the calculation and analysis of the Longnan power grid with small hydropower are carried out, which verifies the feasibility of the proposed method and has reference value for the distribution of network losses of other distributed power grids.

A Survey on the Methods and Results of Data-Driven Koopman Analysis in the Visualization of Dynamical Systems

Koopman mode decomposition is a flow analysis technique developed by Igor Mezic in 2004, based upon the Koopman operator first proposed by Bernard Koopman in 1931. Via Koopman decomposition any non-chaotic well-sampled dynamic system - linear, non-linear, laminar or turbulent - is broken down into single-frequency repetitive components (modes). This paper presents a survey consolidating published information regarding data-driven Koopman analysis techniques. It is intended to aid researchers exploring the suitability of data-driven Koopman analysis in anticipation of developing their own modeling. A basic mathematical explanation of Koopman analysis is given with emphasis toward the data-driven Dynamic Mode Decomposition (DMD) solution, which converges to the Koopman operator given a highly-sampled dataset. The four primary uses of Koopman analysis: flow analysis, power grid analysis, building thermal analysis, and biomedical analysis are discussed, along with other publications. Finally, weaknesses and problems inherent within Koopman analysis/DMD will be enumerated, alongside potential solutions. Koopman analysis is a computationally complex, yet often suitable method for determining periodic motion in any highly-sampled dataset. When compared to a similar analysis method, Proper Orthogonal Decomposition, Koopman analysis often provides additional detail regarding the structure of less significant modes present, albeit at the cost of increased computational complexity.

Real-time online analysis of power grid

The unprecedented operational complexity and security risks in operating the Chinese UHV AC/DC power grid calls for a new generation of real-time (sec-order) online analysis systems to provide decision support. Our recent Digital Twin based online analysis research and development work to address the needs is presented. In addition, a high-level solution architecture for the future real-time online analysis systems is proposed for realizing: a) The delay of the virtual model to mirror the physical power grid is in sub-sec range; b) The downstream online analysis applications could have sec-order responsiveness.

The architecture and key technology of fault inversion systemfor Hybrid UHV AC/DC power system

The architecture and key technology of fault inversion system for Hybrid UHV AC/DC power system are introduced in this paper. The complete and accurate fault inversion model of power grid is formed by integrating the off-line model of Power System Data Base(PSDB) with the real-time online model of Smart Grid Dispatching and Control System(D5000). This model not only solves the problem that the real-time online model lacks transient parameter, but also overcomes the difficulty that the off-line model cannot reflect the real-time operation condition of the power grid, Therefore, this model meets the requirements of timeliness and accuracy of fault inversion calculation. Based on this model, the simulation calculation and analysis of power grid are carried out, and the changes of electricity volume in the process of power grid fault are simulated accurately. The system has been installed successfully and put into use in the simulation centre of the State Grid. The fault inversion analysis is carried out for State Grid Dispatch Centre and East China power grid. Besides, the scientific validity of the proposed scheme is verified.

Proposal of computational tool do support maintenance decision for electric power grids

The Equivalent duration of electric power service interruptions per customer unit (DEC) and the Equivalent frequency of electric power service interruptions per customer unit (FEC) are determinant indexes for the application of penalties by the National Agency for Electrical Power (ANEEL) of Brazil over concessionary companies. Established by ANEEL’s ‘Procedures for Electrical Power Distribution on the National Electrical System’ (PRODIST Module 8), these indexes indicate the continuity of services, consequently the quality of services, must be kept low, therefore methodologies capable to support company´s decisions for cost reduction of operations and penalties by underperformance are always wanted. This analysis of power grids’ failures and repair operations uses information about the electrical distribution of the State of Rio de Janeiro converted into data that includes costs and other details surrounding maintenance operations. The objective is to compute and to find the optimum allocation of resources to attenuate most of the impact of power outages. The discrepancies found along the development of this work had to be controlled by averages, standard deviations and the estimation of uncertainties. The steps were established targeting cost reductions opportunities, based on the several documents such as reports provided to ANEEL by the concessionary company responsible for the power distribution for the City of Rio de Janeiro; the PRODIST Module 8; weather data for the areas where the power grids are located; and the month of the year and number of typical occurrences. The conclusions are limited by the choices of computational methods applied, the quality of the information acquired, and by the efforts spent to adjust and reorganize all the information into data.

Design and Development of Miniature Non-contact Multi-range Current Sensor

High-performance sensors and high-efficiency and accurate measurement technology are the basis of power grid control, monitoring, analysis and decision-making. Current monitoring devices commonly used in power systems cannot meet the need for simultaneous measurement of steady-state current and transient current. In this paper, a high-performance current sensor based on giant magnetoresistance sensor is developed, and key technologies such as sensor structure, sensor chip selection, signal processing circuit and electromagnetic compatibility are designed. The experimental results show that the micro current sensor can accurately measure the steady-state current of 1000A and the transient current of 20kA. At the same time, it has the advantages of small size, high sensitivity, large measurement range, convenient installation and high integration, which meets the measurement requirements of smart grid.

Improved Correction Strategy for Power Flow Control Based on Multi-Machine Sensitivity Analysis

Sensitivity analysis is a conventional power grid analysis method that has been widely used in active power safety correction of power systems. This method relies on linearizing the power flow equation, assuming that the control variable change will continue to act on the power system until a new steady-state operating point is reached. However, this method ignores the quasi-steady-state physical response of power equipment to various control operations, which makes the method not suitable for real-time decision-making. Therefore, according to the improved equal and opposite quantity adjustment principle, this paper proposes a practical power flow correction control strategy, that can avoid repeated and inefficient adjustments of the system. In the proposed method, the single-machine sensitivity matrix under the AC power flow model is obtained based on the Taylor series expansion of the power flow equation. Subsequently, the multi-machine sensitivity with no dependence on the choice of the balancing machine is proposed considering the quasi-steady-state method. The study is verified on a practical system, showing that the proposed method has an error of less than 1% with a rapid computation speed. The proposed method satisfies the requirements of real-time analysis and online control strategies, which can provide a more practical approach for active power safety correction.

Grid dispatching domain division related to large-scale renewable energy grid-connecting

As the proportion of renewable energy access to the grid increases, regional phenomenon of wind power and photovoltaic curtailment highlights. In order to improve the capacity of new energy consumption and alleviate the complexity of power grid scheduling, a regional power grid scheduling domain partitioning method for high proportion of renewable energy access is proposed. The renewable energy regional grid operation domain is divided into normal domain, abnormal domain and emergency domain, and combined with the regulation capability of conventional thermal power units and the power fluctuation ability of energy storage, the divide indicators of each operation domain is defined. Finally, an example analysis of the Dalian regional power grid in Liaoning is carried out to verify the validity and correctness of the grid multi-domain division method.

A Complex-Network Approach to the Generation of Synthetic Power Transmission Networks

The application of complex-network techniques to the analysis of power grids can provide interesting complementary insights into power studies such as the generation of synthetic power grids. Synthetic power grids are realistic, albeit nonreal, case studies that are topologically consistent with respect to real power networks. As a preliminary step to the generation of synthetic power grids, we use complex-network techniques to analyze several European power networks from a topological point of view. We observe that real topologies are markedly heterogeneous. Then, we present our main contribution: a novel model that generates synthetic spatial power networks that mimics the historical evolution of power systems by taking into account economic and technical factors. The model is articulated in two steps—the first step focused on economic efficiency in which power demand is met and the second one is targeted at increasing network robustness while achieving topological attributes. The resulting synthetic networks are topologically consistent with real power grids. The parametrical nature of the proposed model allows the generation of different instances of consistent power networks, a very interesting feature for grid generation.

A PLL-Based Online Estimation of Induction Motor Consumption Without Electrical Measurement

This work is supported by a company wishing to develop new products in the field of energy monitoring in industry. It concerns the real-time estimation of the electrical consumption of an asynchronous motor without electrical measurement. The challenge consists of estimating the characteristic quantities of the motor (speed, torque, powers, efficiency) with only one vibratory measurement, information on the nameplate and commercial documentation available online. To obtain a real-time estimate, traditional FFT analysis is replaced by a PLL initially designed for power grid analysis. So, the second challenge is to modify this PLL for use with vibratory measurement characterized by a low signal-to-noise ratio, amplitude variations and a non-stationary behavior. A complete design and experimental tests are presented to validate the proposed approach.