Stable Operation Of Power System Research Articles

TrendRank method for evaluating the importance of power grid nodes considering information network

To ensure the stable operation of power systems, critical nodes need to be identified for key protection. The LeaderRank algorithm is a fast and accurate algorithm for identifying key nodes, but it has obvious inappropriateness when targeting the power system. For this reason, this paper considers the scheduling function of the information network and the power flow betweenness of the power grid. A TrendRank algorithm is proposed to identify key nodes in complex power grids. TrendRank values can be computed iteratively by a weighted distribution strategy of internally linked nodes and then ranked. The performance of the TrendRank method has been fully tested and benchmarked on IEEE39 and IEEE118. The comparison of four performance metrics fully validates the effectiveness and superiority of the method. The TrendRank algorithm provides an idea in protecting the power system, which makes the economic cost of protecting the power system lower.

Wide-area damping power system control based on robust self-triggered model predictive control

Unavoidable random disturbances during the operation of large-scale interconnected power systems may undermine their stability. To prevent this problem, we propose a damping control method based on robust self-triggered model predictive control. First, we linearize the power system model and determine the controller installation location and feedback signal through geometric measurements. Subsequently, the model order is reduced by applying Schur method for balanced truncation. According to the assumed distribution and boundary range of random disturbance, the multi-cell robust predictive control minimax optimization problem described by the vertex is established. By deriving the upper bound of the maximum problem, the minimax problem is transformed into minimizing the upper bound problem to deal with the uncertain problem, which is transformed into linear matrix inequality for controller design. Finally, we introduce a self-triggered control strategy that avoids solving a constrained optimization problem at every time step. This strategy reduces the number of controller responses and resource requirements for sampling and communication, thereby improving computational efficiency. Selecting the two zone four-machine system as the test system, the simulation results demonstrate that the proposed control strategy can effectively eliminate the effect of random disturbances on the stable operation of power systems, improve the damping characteristics of power systems, and suppress low-frequency oscillations.

Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method had been proposed previously for multiple PV systems using motion estimation. This method forecasts the short time ahead PV power generation by estimating the motion between two geographical images of the distributed PV power systems. In this method, the parameter λ, which relates the smoothness of the resulting motion vector field and affects the accuracy of the forecasting, is important. This study focuses on the parameter λ and evaluates the effect of changing this parameter on forecasting accuracy. In the periods with drastic power output changes, the forecasting was conducted on 101 PV systems. The results indicate that the absolute mean error of the proposed method with the best parameter is 10.3%, whereas that of the persistence forecasting method is 23.7%. Therefore, the proposed method is effective in forecasting periods when PV output changes drastically within a short time interval.

Development of wind-energy modeling technology and standards

To ensure the stable operation of power systems with large proportions of wind power, China has published a series of national, industry, and enterprise standards for wind power. The increase in the number of standards and the expansion of their application scope have given rise to a situation where multiple standards overlap and conflict with regard to the establishment of models and their applicability, resulting in unclear standard application scenarios. Therefore, it is imperative to analyze the development of wind-turbine and wind-farm modeling, along with the relevant standards. This paper presents the methods for wind-turbine modeling, the equivalent model of wind farms based on the general model of wind turbines, and the technical provisions and application scenarios involved in the relevant domestic and international standards. The adaptability of the relevant standards is examined. The results of this study are helpful for advancing wind power generation in China and ensuring the safe and stable operation of large-scale wind power systems.

Fault Diagnosis of Civil Aircraft Electrical Power System Based on Triangular Fuzzy Number and Grey Relational Analysis

Abstract The stable operation of the aircraft electrical power system is closely related to aircraft’s function implementation and safety. Therefore, research on fault diagnosis of aircraft electrical system is important and necessary. This paper proposes a fault diagnosis method of civil aircraft power system based on triangular fuzzy number and grey relational analysis. For the purpose of obtaining complete data, it takes into account single fault symptom and historical data, and keeps the fuzzy feature from initial estimation of diagnosis scheme to the end. The example analysis shows that this method has feasibility and efficiency in practical application.

Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks

As the construction of smart grids is in full swing, the number of secondary equipment is also increasing, resulting in an explosive growth of power big data, which is related to the safe and stable operation of power systems. During the operation of the secondary equipment, a large amount of short text data of faults and defects are accumulated, and they are often manually recorded by transportation inspection personnel to complete the classification of defects. Therefore, an automatic text classification based on convolutional neural networks (CNN) is proposed in this paper. Firstly, the topic model is used to mine the global features. At the same time, the word2vec word vector model is used to mine the contextual semantic features of words. Then, the improved LDA topic word vector and word2vec word vector are combined to absorb their respective advantages and utilizations. Finally, the validity and accuracy of the model is verified using actual operational data from the northwest power grid as case study.

A new approach for optimal photovoltaic resources in distribution networks with reconfiguration based on L_∞

PurposeIn the vast majority of published papers, the optimal allocation of photovoltaic distributed generation (PVDG) units and reconfiguration problems are proposed along with the number of PVDG used in the simulation. However, optimisation without selecting the number of PVDG units installed in the distribution grid is insufficient to achieve a better operational performance of power systems. Moreover, multi-objective installation of PVDG units and reconfiguration aims to simultaneously relieve congestion problems, improve voltage profile and minimise the active and reactive power losses. Therefore, this paper aims to propose a new modified camel algorithm (NMCA) to solve multi-objective problems considering radial distribution system to achieve secure and stable operation of electric power system with good performance.Design/methodology/approachIn this paper, the decision variables include the location and size of PVDG units with specific rang to determine the number of PVDG units needed to install and open network lines determined using NMCA based on the L_∞ technique. This also satisfies the operating and radial constraints. Furthermore, a benchmark comparison with different well known optimisation algorithms has been made to confirm the solutions. Finally, an analysis of the findings was conducted, and the feasibility of solutions was fully verified and discussed.FindingsTwo test systems – the institute of electrical and electronics engineers (IEEE) 33-bus and IEEE 69-bus, were used to examine the accuracy and effectiveness of the proposed algorithm. The findings obtained amply proved the efficiency and superiority of the NMCA algorithm over the other different optimisation algorithms.Originality/valueThe proposed approach is applied to solve the installation PVDG unit’s problem and reconfiguration problem in the radial distribution system, satisfying the operating and radial constraints. Also, it minimises active and reactive power losses and improves voltage profile.

A hybrid ensembled double-input-fuzzy-modules based precise prediction of PV power generation

As one of the most widespread renewable energy technologies, photovoltaic power generation system provides great environmental and economic benefits. Achieving precise prediction for the PV power generation will greatly improve the quality of electric energy and enhance the stability of power system operation. Inspired by the powerful ability of fuzzy logic to deal with uncertainty and the superiority of machine learning to handle the time series prediction, a hybrid ensembled model consisted of the Double-Input-Fuzzy-Modules (DIFM) and Extreme Learning Machine is proposed in this paper. Firstly, the PV power generation data is taken as the input of each DIFM in order to efficiently handle the uncertainties. Then, the outputs of each DIFM are used as the input of ELM. Moreover, the least square estimation is applied to train the parameters of the hybrid ensembled model to further enhance the predict precision. Finally, the proposed hybrid ensembled model is applied to achieve a real-world PV power generation forecasting. The cases study and comparisons results indicate that the proposed hybrid ensembled model outperforms other methods, in terms of the mean absolute error, the root means square error and the coefficient of determination.

A novel hybrid model for short-term prediction of wind speed

Due to the randomness and contingency of wind speed size and direction, it is difficult to predict the wind speed accurately, which seriously affects the stable operation of the power system. To improve the operation stability of power system, the accurate prediction of wind speed is very important. In this paper, a new hybrid model based on gray wolf algorithm (GWO) and support vector machine (SVM) for wind speed prediction is proposed. Firstly, Neo4j(NE) is utilized to identify the data and preprocess the data. Secondly, k-means clustering(KC) is utilized to analyze data and eliminate invalid data. Thirdly, GWO is utilized to optimize the kernel function parameters and penalty factors of SVM to improve the prediction results. Fourthly, The four modules are combined into NE-KC-GWO-SVM model to predict the wind speed accurately. Finally, to verify the effectiveness of the proposed model, the prediction accuracy of the model is experimentally analyzed from two parts. One is to analyze the superiority of the model itself by using the method of single model removed. The results show that the proposed model is the best, and has high accuracy, and can reflect the characteristics of wind speed well and truly. The other one is that models similar to those proposed in the literature are selected for comparative analysis. The experimental results show that compared with the other two models, the proposed model has the best accuracy. At the same time, the proposed model has good prediction stability and acceptable time complexity. Based on all the experimental results, it can be obtained that the proposed model has better prediction effect, which can provide a scientific basis for the macro-control of power system and improve the operation security and stability of power system.

Research on magnetizing inrush current of microgrid based on Grey identification

With the large-scale access of wind and light energy, the power system is gradually developing towards the micro grid with low inertia. AC microgrid often uses multiple parallel transformers to supply power to the load at the same time. In case of power failure due to a fault or maintenance, the parallel transformer needs to be powered on and closed without load to restore power supply. The parallel transformer is easier to saturate, the core remanence is higher, and the peak value of inrush current is larger, which has an impact on the stability of microgrid. It will also lead to misoperation of transformer differential protection and affect the safe and stable operation of power system. Therefore, a switching method of parallel transformer based on Grey recognition is proposed. Firstly, the generation mechanism of inrush current is analyzed theoretically, and then the waveform shape characteristics are analyzed. The grey whitening weight clustering method is used to identify the feature set to complete the magnetizing inrush current locking and ensure that the protection does not misoperate. Finally, the PSCAD simulation model is established to simulate the AC microgrid to verify the feasibility of this method.

Significance of SMES Devices for Power System Frequency Regulation Scheme considering Distributed Energy Resources in a Deregulated Environment

Nowadays, the restructuring of power systems is extremely urgent due to the depletion of fossil fuels on the one hand and the environmental impact on the other. In the restructured environment, the incorporation of renewable energy sources and storage devices is key as they have helped achieve a milestone in the form of microgrid technology. As the restructuring of the power system increases, there are several types of generation sources, and distribution companies express their interest in trading in a deregulated environment to operate economically. When considering the power system deregulation, the contract value deviates in some situations, resulting in an imbalance between the generation and the energy consumption, which can bring the system into a power outage condition. In particular, load frequency control has been a great challenge over the past few decades to ensure the stable operation of power systems. This study considers two generation sources: mini-hydro in GENCO-1 and 3 and microgrid (combination of wind, fuel cell, battery storage, and diesel engine) in GENCO-2 and 4. It is two equal-area networks; in area-1, GENCO-1 and 2, and in area-2, GENCO-3 and 4 are considered, respectively. In addition, a FOPID controller and two ancillary devices, such as a unified power flow controller and a superconducting magnetic energy storage system, have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and agreement violations. The simulation results show that ancillary devices and controller participation significantly enhance the system response by reducing the frequency and tie-line power fluctuation. To validate the efficacy of the proposed method, respective performance indices and percentages of improvement have been obtained. Finally, this study demonstrated the effectiveness of the proposed restructured power system in a deregulated environment.

Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting

The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting (STLF) is critical for the daily operation of power plants, and state-of-the-art approaches for it involve hybrid models that deploy regressive deep learning algorithms, such as neural networks, in conjunction with clustering techniques for the pre-processing of load data before they are fed to the neural network. This paper develops and evaluates four robust STLF models based on Multi-Layer Perceptrons (MLPs) coupled with the K-Means and Fuzzy C-Means clustering algorithms. The first set of two models cluster the data before feeding it to the MLPs, and are directly comparable to similar existing approaches, yielding, however, better forecasting accuracy. They also serve as a common reference point for the evaluation of the second set of two models, which further enhance the input to the MLP by informing it explicitly with clustering information, which is a novel feature. All four models are designed, tested and evaluated using data from the Greek power system, although their development is generic and they could, in principle, be applied to any power system. The results obtained by the four models are compared to those of other STLF methods, using objective metrics, and the accuracy obtained, as well as convergence time, is in most cases improved.

Optimal Operation Of Distribution Network With Photovoltaic

As a clean, renewable and abundant new energy, photovoltaic power generation has gradually gained people’s favor after wind power. Affected by weather, light intensity and temperature, the output of photovoltaic power generation has obvious fluctuation and uncertainty, which seriously threatens the safe and stable operation of power system. Therefore, it is of great practical significance to study the optimal operation of distribution network containing photovoltaic. In this paper, the short-term output characteristics of photovoltaic power generation are studied, and the short-term output model of photovoltaic power generation is established by using the probability distribution of “clear sky index”. On this basis, based on the power flow calculation principle of DistFlow, the operation optimization model of distribution network with photovoltaic power plants is established, and the stochastic characteristics of photovoltaic power generation are flexibly handled by the opportunity constraint method. Using this model, the influence of the stochastic characteristics of photovoltaic power generation on the day-ahead dispatching and reserve capacity of distribution network is calculated and analyzed.

The Technology of Space Electric Field Detection of Deteriorated Insulator Strings Based on Multi Rotor UAV

Porcelain insulator with good insulation performance is the key to the stable operation of power system. In order to characterize its insulation performance safely and accurately, a 1:1 three-dimensional electric field simulation model of 220 kV porcelain tension insulator string is established by using COMSOL finite element software, and a UAV live detection platform for degraded insulator string is built according to the optical electric field sensor. The results show that when the insulator deteriorates, the distribution electric field curve will appear obvious distortion; comparing the simulation results with the live measurement results, it is found that the electric field waveform curve obtained by the live detection platform of degraded insulators based on UAV can be accurate and can effectively judge the insulators with deterioration.

A comprehensive review for wind, solar, and electrical load forecasting methods

Wind power, solar power, and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system. With the increasing permeability of new energy and the rising demand response load, the uncertainty on the production and load sides are both increased, bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy. Most review/survey papers focus on one specific forecasting object (wind, solar, or load), a few involve the above two or three objects, but the forecasting objects are surveyed separately. Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides. However, there is no corresponding review at present. Hence, our study provides a comprehensive review of wind, solar, and electrical load forecasting methods. Furthermore, the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript. Challenges and future research directions are discussed at last.

Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

Wind energy has been connected to the power system on a large scale with the advantage of little pollution and large reserves. While ramping events under the influence of extreme weather will cause damage to the safe and stable operation of power system. It is significant to promote the consumption of renewable energy by improving the power prediction accuracy of ramping events. This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network. Firstly, the spinning door algorithm builds parallelograms to identify ramping events from historical data. Due to the rarity of ramping events, the serious shortage of samples restricts the accuracy of the prediction model. By using generative adversarial network for training, simulated ramping data are generated to expand the database. After obtaining sufficient data, classification and type prediction of ramping events are carried out, and the type probability is calculated. Combined with the probability weight, the spatiotemporal neural network considering numerical weather prediction data is used to realize power prediction. Finally, the effectiveness of the model is verified by the actual measurement data of a wind farm in Northeast China.

Methods of anomaly state detection for power systems based on bilateral cyber‐physical information

Cyberattacks have emerged as novel threats to modern power systems. By exploiting the vulnerabilities of insecure devices, an attacker can inject viruses to lurk and collect system conditions through sniffing and then launch well-designed attacks. Collaboratively applying bilateral cyber-physical information can help to detect anomalous system states caused by sniffing, which can isolate virus impacts on the cyber side and ensure the stable operation of power systems. Here, a dynamic weight ensemble isolation forest algorithm is proposed to mine anomaly system states utilizing bilateral information based on the hypothesis that the fused cyber-physical system state under assault has the shortest average path length in a constructed random forest. In addition, the proposed algorithm is able to realize improved performance in power system anomaly status mining and adapt to constantly changing power systems. The method is verified by simulations on a co-simulation platform. The results show that the method outperforms other anomaly detection methods.

Analytical Representation of Data-driven Transient Stability Constraint and Its Application in Preventive Control

Accurate transient stability assessment (TSA) and effective preventive control are important for the stable operation of power systems. With the superiorities in precision and efficiency, data-driven methods are widely used in TSA nowadays. Data-driven TSA model can be adopted in the stability constraint of preventive control optimization, but existing methods are mostly iteration-based methods, which may result in low efficiency, sometimes even non-convergence. In this paper, an analytical representation method of data-driven transient stability constraint is proposed based on a nonparametric regression model built for TSA. Key feature extraction and dominant sample selection are proposed to reduce the scale of the TSA model, and bi-level linearization is applied to further modify it. Optimal preventive control model is then formulated as a Mixed-integer Linear Program (MILP) problem with the linearized analytical data-driven transient stability constraint, which can be solved without iterations. An overall procedure of data-driven TSA and preventive control is finally developed. Case studies show that the proposed method has high accuracy in TSA and can achieve effective preventive control of power system with high efficiency.

Deep Primary and Secondary Fusion Transformer Based on Internet of Things Technology

As one of the core equipment of power system, power transformer has great fault influence and complex fault causes. In order to ensure the safe and stable operation of power system, its operation state must be monitored and judged. With the increasing maturity of the Internet industry and the continuous development of sensor technology, the emergence of smart grid has contributed to the realization of intelligent transformer on-line monitoring, condition evaluation and fault diagnosis. This paper studies the deep primary and secondary fusion transformer based on the Internet of Things technology, summarizes the development status of intelligent transformer and its existing problems on the basis of relevant literature, and proposes the optimization technology of deep primary and secondary fusion transformer based on the Internet of Things technology to solve its existing problems, and conducts related experiments and fault detection research on the proposed technology. The experimental results show that this optimization technology has good feasibility, good self-state evaluation and fault diagnosis functions. The deep primary and secondary fusion based on the Internet of Things technology proposed in this study can increase the reliability monitoring of intelligent transformer operation, provide a strong technical guarantee for the normal operation of distribution network, and also provide important technical support for the next research of distribution network technology.

A Physics-Informed Deep Learning Paradigm for Transient Power Angle Stability Assessment

Transient stability assessment (TSA) plays an essential role in the safe and stable operation of power system. The traditional time-domain simulation method and direct method have great limitation in modeling and computing, which can not meet the need of fast and accurate assessment of power system stability. Therefore, combined physics model with data-driven algorithm, this paper proposes a physics-informed deep learning paradigm to solve this problem. In the data-driven algorithm, recurrent neural networks are trained to predict the rotor speed and power angle with the guiding of the power system’s physical information. Finally, the simulation on IEEE14 system is carried out by using PSS/E software, which proved that the proposed method can predict the power angle trajectory quickly and accurately.