Power Flow Convergence Research Articles

An intelligent analysis method for power flow of bulk power grid based on fusion of indicator system feature and image feature

Abstract At present, the adjustment of power flow non-convergence in large power grid is still mostly based on manual trial and error, which relies heavily on expert experience and is inefficient. Therefore, an intelligent power flow analysis method based on index system and image feature fusion is proposed. Firstly, the power flow index system is constructed according to the actual demand, which can provide data support for the following power flow convergence judgment problems. Secondly, taking into account the constraints of similarity and ternary loss, the image feature information is used to guide the output of the information feature representation and to realize the organic interaction of the two modal features. On this basis, the calculation result of the index is correlated with the actual state of power flow to judge the convergence of power flow. Finally, the proposed method is verified on the CEPRI36 nodes system and a large power grid in China. The simulation results show that the accuracy of power flow convergence judgment is 100% for CEPRI36 nodes system and 99.83% for some domestic large power grid. It can effectively predict the convergence of the power flow before the calculation of the power flow required by the project and can provide the basis and theoretical support for the power flow optimization adjustment based on the indicator data.

Convergence criterion of power flow calculation based on graph neural network

In order to solve the problem of current data-driven power flow calculation methods rarely consider the divergence of power flow, which always maps a false system power flow when a divergence power flow case was given, a data-driven power flow convergence method based on DGAT-GPPool graph neural network classifier is proposed. Firstly, to solve the problem that the classical graph convolution method does not consider the edge attribute, a double-view graph attention convolution layer is constructed based on line admittance. Secondly, to solve the existing pooling method also does not consider the edge attribute and the loss of physical meaning of the coarse graph obtained from pooling, a grid partition pooling layer is constructed based on the electrical distance between nodes. Finally, 10000 system samples containing different network topologies are generated based on the IEEE 14-node system and its extended system, the accuracy reaches 99.3% in the testing set after training, and the effectiveness of the improvements in graph convolution and graph pooling is verified by comparative experiments.

Improvement of Fuzzy Newton Power Flow Convergence

In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the problem of initial value deviation. Since the Jacobian matrix has a significant impact on the convergence region of the power flow, this paper reconstructs the Jacobian matrix based on the Levenberg–Marquardt method, effectively enlarging the convergence region. Through validation experiments on the IEEE 118 standard nodes and simulation comparative analysis, the results confirm the method’s effectiveness in resolving the problem of initial value deviation and notably enlarging the convergence region, thereby improving the convergence of power flow calculations.

Data-driven methods for situation awareness and operational adjustment of sustainable energy integration into power systems

In the context of increasing complexity in power system operations due to the integration of renewable energy sources, two main challenges arise: accurate short-term wind power forecasting and power flow convergence control. Accurate wind power forecasting plays a crucial role in power system scheduling, while controlling power flow convergence is essential for system stability. This study proposes a concise short-term wind power generation prediction model that combines a feature selection-based convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) model. By effectively screening multidimensional feature datasets, the model optimizes the selection of highly correlated feature parameters and assigns weights to input data based on feature correlation. The CNN-BiLSTM combination model is then employed to establish a predictive model for wind power generation based on multiple features. Additionally, this study introduces an automatic adjustment model for power flow convergence using the D3QN (Double Dueling Q Network) reinforcement learning algorithm. This addresses the challenge of power imbalance leading to flow non-convergence, enabling effective control of power flow convergence and adaptive adjustment of operating modes. Experiments conducted using the KDD Cup 2022 wind power prediction dataset validate the wind power prediction method. The results demonstrate that the CNN-BiLSTM model effectively utilizes time-series data, surpassing other neural networks in prediction accuracy. Simulation results based on the PYPOWER case39 standard case reveal that the reinforcement learning model’s reward value increases with training rounds and stabilizes at 40. Remarkably, more than 72% of abnormal flow samples achieve rapid convergence within 10 steps, affirming the proposed method's efficacy and computational efficiency. The findings of this study contribute to enhancing the accurate awareness of new energy integration into power systems and provide a novel adaptive control method for power flow.

A Diagnosis Method of Power Flow Convergence Failure for Bulk Power Systems Based on Intermediate Iteration Data

Power flow calculation is the foundation of security analyses in a power system, and the phenomenon of convergence failure is becoming more prominent with the expansion of the power grid. The existing convergence failure diagnosis methods based on optimization modeling and local feature recognition are no longer viable for bulk power systems. This paper proposes a diagnosis method based on intermediate iteration data and the identification of the transmission power congested channel. Firstly, the transmission power congestion index is constructed, and then a method for identifying transmission congestion channels is proposed. The reasons for convergence failure of the power flow are diagnosed from two aspects: excessive power to be transmitted and insufficient transmission capacity. Finally, with the aim of alleviating transmission channel congestion, a correction strategy for power flow injection space data was constructed, which generates relaxation schemes for operational variables. The effectiveness of the proposed strategy was verified using the simulation results of an actual provincial power grid and a standard example power system with 13,659 buses. The method proposed in this paper is entirely based on intermediate power flow iteration data, which avoids the complex modeling of the power flow adjustment and provides methodological support for power flow diagnosis in bulk power systems.

Limiting the Failure Impact of Interdependent Power-Communication Networks via Optimal Partitioning

The mutual dependency between the power grid and communication network increases the risk of large-scale cascading failure, making the mitigation of outages a difficult task. In this paper, we use the concept of joint partitioning of the power and communication subsystems by identifying the most vulnerable typed-graphlets. This results in higher-order partitions, which prevent cascading failures from propagating globally in the system. For this purpose, we first formulate the problem of partitioning the interdependent system as a large-scale optimization problem with the objective of minimizing the overall load shedding subject to: i) power flow convergence, ii) power stability, iii) partition connectivity, iv) communication congestion/delay control, and v) a high partition quality. Then, using Benders’ method, we solve the problem by decomposing it into a relaxed master problem that consists of constrained spectral clustering, and a linear subproblem that consists of minimizing the load shedding subject to power flow convergence, stability, and congestion/delay control. Simulations are conducted on an IEEE 118-bus supported by a 118-node communication network. Our investigations reveal an average of 62% decrease in damage when the system is optimally partitioned.

Reactive Power Flow Convergence Adjustment Based on Deep Reinforcement Learning

Reactive Power Flow Convergence Adjustment Based on Deep Reinforcement Learning

A Zero-injection Initialization Numerical Method for Ill-conditioned Power Flow

Regarding the initial value sensitivity issues commonly found in Newton-like power flow calculation algorithms, a zero-injection initialization numerical method for ill-conditioned power flow is proposed in this paper, which improves the convergence of power flow calculation in ill-conditioned power systems. Based on the initial power flow provided by this method, the unbalanced active power of each bus (the slack bus excluded) relates to the net injection of active power of that bus and the unbalanced reactive power of each PQ bus relates to the net injection of reactive power of that bus, thus avoiding significant power unbalance. The method put forward herein can provide a reasonable initial value for the power flow calculation so that difficulty in power flow convergence caused by the unreasonable initial power flow is effectively avoided. The Newton’s method and the optimal multiplier method are implemented respectively on a small-scale ill-conditioned test system to verify the feasibility of the proposed zero-injection initialization numerical method.

A Method of Power Flow Database Generation Base on Weighted Sample Elimination Algorithm

With the rapid development of urban power grids, distributed renewable energy sources and adjustable loads have increased significantly, resulting in more complex operation conditions, increasing the difficulty of power flow calculations. The usage of artificial intelligence technology to assist in calculating power flows for large-scale urban grids has a wide range of application prospects. It is currently difficult to generate enough grid operation database with controlled distribution for artificial intelligence (AI) method research. Data is one of the important factors affecting the performance of deep learning algorithms, and the lack of research on data distribution characteristics also hinders the performance of deep learning algorithms. The distributional characteristics of data sets in high-dimensional feature spaces are difficult to represent and measure, and the algorithm design process is prone to encounter curse of dimensionality. This paper proposed a novel method for generating databases to improve the solving efficiency of data-driven power flow calculation problems. The proposed method removes samples based on the characteristics of data distribution. It constructs two databases, namely the blue noise distribution database and the variable density boundary enhanced distribution database. Compared with the classical stochastic sampling database, the proposed boundary-enhanced variable density (BEVD) database has significantly improved the judgment accuracy of power flow convergence. Finally, the China Electric Power Research Institute-36 (CEPRI-36) bus system is used to verify the effectiveness of the proposed method. The judgment accuracy was improved by 2.91%–9.5%.

Convergence Adjustment Method Based on Approximate Power Flow and Voltage Stability

Prone to problems flow calculation does not converge at the scheduled maximum power operating mode, the current operating personnel inability to visually observe the system state, convergence can only be adjusted by trial and error approach. Solving the problem of non convergence of power flow plays an important role in power system analysis. Based on the intermediate process of solving the Newton method, the concept of an approximate fashion, the relation between convergence and voltage stability is calculated by analyzing the trend of the voltage characteristics similar trend as the main basis of the judgment result in the trend does not converge, based on voltage sensitivity further, by improving the convergence of the method to boost the voltage level indirectly. Finally, after the New England 39-bus system to verify, by demonstrating the relevance and accuracy of the proposed method. When not solve the problem of the convergence trend is not impressive, it has a certain reference value for practical application. This study has a good application prospect in practical engineering application

Power System Flow Adjustment and Sample Generation Based on Deep Reinforcement Learning

With the increasing complexity of power system structures and the increasing penetration of renewable energy, the number of possible power system operation modes increases dramatically. It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis. At present, problems of low efficiency and long time consumption are encountered in the formulation of operation modes, resulting in a very limited number of generated operation modes. In this paper, we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning. First, a discriminator is trained to judge the power flow convergence, and the output of this discriminator is used to construct a value function. Then, the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment. Finally, a large number of convergent power flow samples are generated using the learned adjustment strategy. Compared with the traditional flow adjustment method, the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model. Therefore, this strategy can be automatically learned without manual intervention, which allows a large number of different operation modes to be efficiently formulated. The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.

Using virtual buses and optimal multipliers to converge the sequential AC/DC power flow under high load cases

The sequential AC/DC power flow method has been practically employed to solve power flow for systems with HVDC lines, because the sequential method is well aligned with existing Newton–Raphson method for AC systems. However, it is known to be difficult to converge when the HVDC network is highly loaded. In this paper, an in-depth analysis of the divergence of the sequential method under heavy DC power is given. Then, an enhanced approach based on the sequential AC/DC power flow method is proposed to improve power flow convergence under high load cases without compromised accuracy. Two critical techniques, virtual buses and optimal multipliers, are proposed to address the challenge. First, the decoupled AC grid is augmented with virtual buses which enhance the robustness of calculation for inner loops. Then, the inner Newton–Raphson method is combined with optimal multipliers to ensure convergence. Test results with the modified IEEE 14-bus and 300-bus systems demonstrate the effectiveness of the proposed approach and the successful convergence under high load cases which cannot be achieved by conventional sequential AC/DC power flow.

Power Flow Convergence and Reactive Power Planning in the Creation of Large Synthetic Grids

To encourage and support innovation, synthetic electric grids are fictional, designed systems that mimic the complexity of actual electric grids but contain no confidential information. Synthetic grid design is driven by the requirement to match wide variety of metrics derived from statistics of actual grids. In order to scale these systems to 10,000 buses or more, robust reactive power planning is needed, accounting for power flow convergence issues. This paper addresses reactive power planning and power flow convergence in the context of large synthetic power grids. The iterative algorithm presented by this paper supplements a synthetic transmission network that has been validated by a dc power flow with a realistic set of voltage control devices to meet a specified voltage profile, even with the constraints of difficult power flow convergence for large systems. The algorithm is illustrated with an example new synthetic 10,000 bus system, geographically situated in the western United States, which is publicly available and useful for a variety of research studies. An analysis is shown validating the synthetic system with actual grid characteristics.

Research on Adjustment Method of Power Flow Convergence Based on Planning Model

Power flow calculation is an essential component of power network planning. The problem of its convergence adjustment has a great significance for research. This paper proposes a power flow convergence adjustment method based on a nonlinear programming model. It aims at the non-convergence problem of power flow calculation caused by reactive power imbalance. By introducing a set of slack variables into the AC/DC power flow equations, the conventional power flow calculation is converted to a nonlinear programming model with the least square sum of slack value. Based on the characteristics of node slack value and its distribution law, the power flow reactive power adjustment method and its implementation steps are established, according to the electrical distance. The actual grid planning data of a certain place in China is simulated and calculated, which verifies the effectiveness and feasibility of the method.

The initial guess estimation newton method for power flow in distribution systems

With the increasing integration of distributed generations U+0028 DGs U+0029, there is a demand for DGs to play a more important role on the voltage regulation. Meanwhile, the high penetration of DGs could raise a technical problem that the distribution system may operate with bi-directional power flow, leading to the inadequacy of the traditional power flow. Considering this new scenario in distribution system power flow, the convergence theorem is proposed, which contributes to develop a novel selection method of the initial guess closed to the convergent solution. Moreover, to ensure the fast rate of power flow convergence, the theorem of the maximum iterations estimation is also proposed. Based on the two proposed theorems, an Initial Guess Estimation Newton method is proposed, considering different operational status of DGs and initial guess sensitivity simultaneously. Based on the standard node systems, Tongliao grid, and 69 system of USA, three simulation examples are provided to illustrate the effectiveness of the proposed method.

Production of photonic nanojets by using pupil-masked 3D dielectric cuboid

Photonic nanojets can be created via plane wave irradiation of multi-shaped mesoscale dielectric particles, and a waist of full-width at half-maximum (FWHM) smaller than the diffraction limit can be achieved in this process. In this paper, photonic nanojet produced by a pupil-masked 3D dielectric cuboid lens is numerically investigated under the irradiation of 532 nm wavelength plane wave. It is found that a pupil-masked cuboid lens is not only able to produce photonic nanojets with shorter FWHMs, but also increase its maximal intensity at certain masking ratios on the receiving surface. This phenomenon is different from the result of the spherical-lens reported in previous publications, and is attributed to the convergence of power flow and near-field numerical aperture (NA) increase after analysis of simulated power flow diagrams.

Hybrid Multistarting GA-Tabu Search Method for the Placement of BtB Converters for Korean Metropolitan Ring Grid

This paper presents a method to determine the optimal locations for installing back-to-back (BtB) converters in a power grid as a countermeasure to reduce fault current levels. The installation of BtB converters can be regarded as network reconfiguration. For the purpose, a hybrid multistarting GA-tabu search method was used to determine the best locations from a preselected list of candidate locations. The constraints used in determining the best locations include circuit breaker fault current limits, proximity of proposed locations, and capability of the solution to reach power flow convergence. A simple power injection model after applying line-opening on selected branches was used as a means for power flows with BtB converters. Kron reduction was also applied as a method for network reduction for fast evaluation of fault currents with a given topology. Simulations of the search method were performed on the Korean power system, particularly the Seoul metropolitan area.

Impact of selection of DC base values and DC link control strategies on sequential AC-DC power-flow convergence

This paper demonstrates the convergence of the integrated AC-DC power-flow algorithm as affected by the selection of different base values for the DC quantities and adoption of different control strategies for the DC link. For power-flow modeling of integrated AC-DC systems, the base values of the various DC quantities can be defined in several ways, giving rise to different sets of per-unit system equations. It is observed that different per-unit system models affect the convergence of the power-flow algorithm differently. In a similar manner, the control strategy adopted for the DC link also affects the powerflow convergence. The sequential method is used to solve the DC variables in the Newton Raphson (NR) power flow model, where AC and DC systems are solved separately and are coupled by injecting an equivalent amount of real and reactive power at the terminal AC buses. Now, for a majority of the possible control strategies, the equivalent real and reactive power injections at the concerned buses can be computed a-priori and are independent of the NR iterative loop. However, for a few of the control strategies, the equivalent reactive power injections cannot be computed a-priori and need to be computed in every NR iteration. This affects the performance of the iterative process. Two different per-unit system models and six typical control strategies are taken into consideration. This is validated by numerous case studies conducted on the IEEE 118-bus and 300-bus test systems.

English

In recent years, voltage instability and voltage collapse have been observed in power systems of many countries. Such problems have occurred even more often in developed countries because of utility deregulation. Currently, voltage security is of major importance for successful operation of power systems. Assessment of voltage security is needed to utilize power transmission capacity efficiently and to operate the system uninterruptedly. In our research work, we study voltage security of on power system of IEEE20 bus system. We assess voltage security of these systems using P-V curve In other words, we compute margins of real power(P) . To achieve what we promise, we first obtain the power-flow solution for the given data by running the power-flow program that we have coded using MATLAB. The solution is taken as the base case. Second, we choose candidate buses at which we incrementally change real power for plotting P-V curves. The candidate buses are of load buses so that P margins are computed against increase in demand. Third, we run the power-flow program for incremental changes in real power at the candidate bus. The point at which power-flow convergence is no more available is the critical point. Up to the critical point, we obtain voltages at the candidate bus corresponding to changing realpower at the same bus.Fourth, we plot PV curves using voltages versus P .Finally, the difference between the real power demanded the candidate bus at the critical point and that at the base case gives us the P-margin computed for the candidate bus. This paper proposed that Our investigation of power-flow solutions, P-V curves of the 20 bus IEEE system -shows that we obtain power-flow solution using Newton-Raphson method and compute margins of voltage security with predefined tolerance using MATLAB. Such computations provide us indispensable information for the secure and efficient operation of power systems.

Impact of DC link control strategies on the power-flow convergence of integrated AC–DC systems

Abstract For the power-flow solution of integrated AC–DC systems, five quantities are required to be solved per converter, against three independent equations available. These three equations consist of two basic converter equations and one DC network equation, corresponding to each converter. Thus, for solution, two additional equations are required. These two equations are derived from the control specifications adopted for the DC link. Depending on the application, several combinations of valid control specifications are possible. A set of valid control specifications constitutes a control strategy. It is observed that the control strategy adopted for the DC link strongly affects the power-flow convergence of integrated AC–DC systems. This paper investigates how different control strategies affect the power flow convergence of integrated AC–DC systems. Sequential method is used to solve the DC variables in the Newton Raphson (NR) power flow model. Seven typical control strategies have been taken into consideration. This is validated by numerous case studies carried out with multiple DC links incorporated in the IEEE 118-bus and 300-bus test systems.