Power System Security Analysis Research Articles

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

Power Load Forecast Based on CS-LSTM Neural Network

Load forecast is the foundation of power system operation and planning. The forecast results can guide the power system economic dispatch and security analysis. In order to improve the accuracy of load forecast, this paper proposes a forecasting model based on the combination of the cuckoo search (CS) algorithm and the long short-term memory (LSTM) neural network. Load data are specific data with time series characteristics and periodicity, and the LSTM algorithm can control the information added or discarded through the forgetting gate, so as to realize the function of forgetting or memorizing. Therefore, the use of the LSTM algorithm for load forecast is more effective. The CS algorithm can perform global search better and does not easily fall into local optima. The CS-LSTM forecasting model, where CS algorithm is used to optimize the hyper-parameters of the LSTM model, has a better forecasting effect and is more feasible. Simulation results show that the CS-LSTM model has higher forecasting accuracy than the standard LSTM model, the PSO-LSTM model, and the GA-LSTM model.

Evaluation and Improvement of Power System Security with the Application of Machine Learning

The electricity grid has added many renewable and non-renewable energy sources to meet expanding demand. Sudden load variations exacerbate generator, transmission line, and distribution network issues. Load modelling choices are crucial for system prediction. This study indicates that ZIP load models with contingency criteria can accurately forecast load behaviour over time. The NR method predicts the contingency ranking with the High Bride Line Stability Ranking Index (HLSRI) under single line outage conditions, and an artificial neural network (ANN) is trained to predict the severity of the line outage and the system's behaviour. A mathematical model was utilised to analyse stability and cost with and without the UPFC and IPFC. Machine learning (ML) is used to rapidly predict the most affected transmission line during a contingency by clustering data using the J48 algorithm for the location of compensating devices. The PSO algorithm is used to develop an objective function to minimise fuel costs by maximising generating capacity. A transmission line failure and load variation might damage the electrical system. This study prioritises transmission line breakdowns and load changes. Power system security analysis provides power system status.

Identification of Critical Nodes in Power Grid Based on Improved PageRank Algorithm and Power Flow Transfer Entropy

Identifying critical nodes in the power grid is a crucial aspect of power system security and stability analysis. However, the current methods for identification fall short in fully accounting for the power transfer characteristics between nodes and the consequences of node removal on the security and stability of power grid operation. To enhance the effective and accurate identification of critical nodes in the power grid, a method is proposed. This method is based on improved PageRank algorithm and node-weighted power flow transfer entropy, referred to as IPRA-PFTE. Firstly, based on the power flow and equivalent impedance between nodes, and the introduction of virtual nodes, an improved PageRank algorithm is obtained. Then the node-weighted power flow transfer entropy is derived by considering the uniformity of the transfer power flow distribution in the system following the removal of a node. Finally, the importance of nodes is obtained by combining the improved PageRank algorithm with the node-weighted power flow transfer entropy. The method’s effectiveness and accuracy are validated through simulation using the IEEE 39-bus example and subsequent comparison with existing methods.

A hybrid VMD based contextual feature representation approach for wind speed forecasting

Accurate wind speed prediction is critical for efficient power system operation, regulation, security analysis, and energy trading. However, the stochastic nature of the wind makes wind speed forecasting (WSF) difficult. Thus, a novel hybrid WSF approach termed VMD-Ts2Vec-SVR comprising variational mode decomposition (VMD), contextual time series representation (Ts2Vec) model, and support vector regression (SVR) is proposed. In the proposed approach, VMD is used to decompose the raw input wind speed for denoising, and extracting the main features of the original series, Ts2Vec model is used to learn the sequential contextual representations in all semantic levels from the denoised series, and SVR is used to predict the future wind speed from the contextual representation. Two experiments are performed for testing the proposed approach using wind speed dataset collected from Leicester, and Portland wind farms. For validation of the proposed approach for different time intervals, it is tested for 5-min, 10-min, 15-min, 30-min, 1-h, and 2-h ahead WSF. The performance of the proposed approach is compared with seven individual models, seven hybrid VMD based models for better validation. Two experiments demonstrated both the proposed approach’s superior performance across all time horizons and its viability for the WSF.

Inertial security region estimation and analysis of new power systems considering renewable energy virtual inertial

With the proposal of “double carbon strategy” and new power system construction, the increasing proportion of renewable energy sources and the wide access of power line sub-devices, the inertia support capacity of the power grid is gradually weakened and shows low inertia characteristics, which seriously threatens the system operation safety and frequency stability. In order to comprehensively grasp the operation status of the new power system and ensure its frequency stability, this paper proposes a renewable energy virtual control theory based on the established frequency response model of new power systems considering multiple inertial resources. Secondly, the inertial safety region (ISR) of the new power system considering the virtual inertia and frequency regulation capability of renewable energy is proposed to determine the frequency stability boundary, and its estimation method and procedure are introduced in detail. Finally, a case study and analysis are conducted in the New England system with actual operational data to verify the feasibility and effectiveness of the proposed method. The simulation results show that the method can effectively identify the ISR of the new power system and analyze the mechanism of the influence of renewable energy on ISR, and relevant conclusions are drawn from the simulation results.

Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique, in the evaluation of the severity index, clustering and loading suffer from more iteration leading to more computational delay. Hence this research article identifies, a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects. The polynomial load modelling or ZIP (constant impedances (Z), Constant Current (I) and Constant active power (P)) is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security. The process of finding the severity of the line using a Hybrid Line Stability Ranking Index (HLSRI) is used for assisting the concepts of machine learning with J48 algorithm, infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability. The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system. The Unified Power Flow Controller (UPFC), facts devices for the purpose of compensating the losses by maintaining the voltage characteristics. The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.

PREVENTING AND MINIMIZING ARC FLASH RISK IN POWER SYSTEM NETWORK

Arc flash events have resulted in several accidents due to faults in electrical equipment that lead to a significant release of energy. This event is a hazard and threat to the Power System Security and due to the large energy release, plasma is generated, as pressure increases since it causes physical damage to equipment while life of system operators within the vicinity of its occurrence are at risk. Although arc flash is one of the electrical safety programs that have been in existence, arc flash hazard was not adequately addressed until recently. However, the Electric Arc phenomenon is relatively new in the Nigerian power industry, there are certain aspect that are yet to be treated by the available literature, hence it is the duty of this work to establish model for addressing the observed lapses. The design is adequately prepared for the power system security analysis using typical scenarios in industrial facilities that are prone to yield high incident energy levels. In line with the foregoing, the developed methodology is validated using a segment of the Nigerias power industry as case study.

Equivalent Method for Nonreciprocal Networks Based on Sensitivity Consistency

An equivalent method for a nonreciprocal network is proposed. The method introduces controlled current sources into an equivalent network to preserve the nonreciprocal characteristics of the original network. The parameters of the equivalent network are derived under the condition of keeping the network sensitivities at the internal network and border nodes unchanged before and after equivalence. Compared to the existing equivalent methods, which are restricted to reciprocal networks and ignore the consistency of network sensitivities, the proposed method is more adaptable and accurate when applied in practical nonreciprocal networks, such as power systems. This method is applied to static security analysis of an IEEE 39-bus power system and a utility 661-bus power system to illustrate its effectiveness and accuracy in practical systems.

Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis

Power System Security and Contingency analysis is one of the most important tasks in power systems. In operation, contingency analysis assists engineers to operate the power system at a secure and safe operating point where equipment are loaded within their safe operating area (SOA). Power is dispatched to customers with acceptable quality standards. The results of off-line load flow calculations are used to estimate performance indices (PI flow, PI V). MATLAB toolbox was the proposed methodology used for the implementation. The proposed approach for contingency analysis was found to be appropriate for screening and ranking fast voltage and line flow contingencies.

A method of multivariate short-term voltage stability assessment based on heterogeneous graph attention deep network

Compared with time-domain simulation (TDS), data-driven models show great advantage in time consumption of the power system security analysis. This paper proposes a novel graph neural network (GNN) based model for the short-term voltage stability (STVS) assessment. Based on mainly the steady-state information as the inputs, the model can provide multivariate stability indices for each bus and adapt to minor topological changes. Both the fast voltage collapses (FVC) and fault-induced delayed voltage recovery (FIDVR) events can be identified. A modified multi-task learning (MTL) process improves the multi-index evaluation performance of the model. As the kernel of the model, a heterogeneous graph attention deep network (HGAT) with various types of nodes works to aggregate the local information and generate new features. Then several micro-detectors scan over the network to provide the STVS indices of each bus with only the feature vector of one node as input for the prediction. Comparisons on the test system show that the proposed method can provide fine-grained STVS evaluation and show good robustness in the scenarios of different dynamic load distribution and network topology changes.

Advance Evaluation of System Protection Control Strategy Based on Multi-scheme Concurrency

According to rapid assessment requirement of system protection control strategy adaptability in future operation mode, combining with application status of distributed computing for power system security and stability analysis, based on design idea of asynchronism and concurrency, this paper proposes a multi-scheme concurrent computing framework suitable for coarse-grained case parallelism. By the way of multi-data section parallel, the asynchronous and concurrent scheduling management of computing task of multiple application functions under different sections is realized, which effectively improves the utilization rate of computing resources and significantly reduces the computing period of advance evaluation of system protection control strategy adaptability. The technology has been applied in provincial power grid and its effectiveness has been verified.

Contigency Analysis in Multiline Distributed System using Dynamic Voltage Restorer

Power system security analysis involves a vital role in improving the system security and helps preventing the system collapse condition. This paper shows the conception and simulation of nine bus system with generator outage contingency. The objective of this work is to improve the real and reactive powers by employing DVR. Nine bus systems with generator outage contingency condition healthy, unhealthy without IDVR and unhealthy with DVR are simulated. The comparative study is presented to demonstrate the improvement in the power quality by adding DVR. The results indicate that real and reactive powers are improved by adding DVR.

Real-time dynamic security analysis of power systems using strategic PMU measurements and decision tree classification

Fast and accurate online dynamic security analysis (DSA) is the key enabler for secure operation of modern power systems. Real-time assessment of the current power system operating state and increased awareness about plausible future insecurity can enable necessary operational and control measures to ensure secure operation. This paper proposes an ensemble decision tree (DT)-based online DSA method for large-scale interconnected power system networks using wide area measurement (WAMS) with phasor measurement units (PMU). A novel attribute selection method has been demonstrated for optimizing PMU installation at strategic buses in large-scale power networks. Multi-stage screening of the initial measurements has been done to minimize the data acquisition cost and computation overhead, which are the key challenges in real-time DSA. The ensemble DT classifier was trained offline using data from the operational model of the power system under different system loading and contingency conditions. The trained classifier provides online security assessment and classifies the power system’s current operating state as secure or insecure based on real-time measurements of the key attributes by selective PMUs. The proposed scheme was tested on IEEE 118-bus system, and the results demonstrate that it has the potential to be used as a reliable online DSA method.

Wind speed forecasting based on variational mode decomposition and improved echo state network

Accurate wind speed forecasting is conducive to power system operation, peak regulation, security analysis, and energy trading. This study proposes a hybrid model named VMD-DE-ESN incorporating variational mode decomposition (VMD) and differential evolution (DE) and echo state network (ESN) for wind speed forecasting. In the proposed model, VMD is applied to decompose the wind speed series to eliminate noise and mine the main features of original series, DE is utilized to optimize three important parameters of echo state network, and the improved ESN is used to forecast each decomposed subseries. The final forecasting results are obtained by summarizing the forecasting results of all subseries. To validate the accuracy and stability of the proposed model, four wind speed datasets collected from Sotavento wind farm in Galicia, northwestern Spain are used for forecasting. Mean absolute percentage errors of the proposed model in four datasets are 2.0161%, 3.4153%, 2.1544%, and 2.8478% respectively, which are much lower than those of nine comparative models. Therefore, the proposed model has satisfactory performance and is suitable for wind speed forecasting.

An Improved Artificial Physics Optimization Algorithm Approach for Static Power System Security Analysis

The major functions involved in power system security analysis are security assessment and security enhancement. The contingency analysis plays a vital role in the security assessment which has been carried out based on the performance index. Security enhancement incorporates security constrained optimal power flow (SCOPF) which ensures that system is operating at normal state by taking preventive and control actions. An improved artificial physics optimization (APO) evolutionary algorithm is presented in this paper for solving SCOPF problem. The trial and error approach is used for selection of gravitational constant (G) in standard APO method. In order to circumvent this problem, a fuzzy approach is used to tune the gravitational constant (G). Simulations are done on IEEE 30-bus system, Indian 75-bus system, and IEEE 118-bus test system. To evaluate the robustness, results obtained using the proposed method are compared with results obtained using standard conventional algorithms, namely APO, Bat algorithm, and genetic algorithm. The obtained results indicate that the proposed fuzzy adaptive artificial physics optimization (FAAPO) method is efficient as well as robust for solving SCOPF problem to get near global optimization solution compared to standard APO algorithm and other heuristic methods reported in the literature.

Network partitioning approach for reactive power/voltage control using analytical nodes coupling expressions

Power-grid partitioning is an important prerequisite for power systems security analysis and control. In this study, a novel recursive grid bipartitioning strategy is proposed. First, two analytical expressions are derived from Kirchhoff's equations to represent coupling relationships between nodes. Then, generator nodes under study are divided into two groups by recursively applying the coupling relationships between generator nodes. Finally, the coupling relationships between generator and load nodes are used to merge relevant load nodes to the corresponding groups of generators. The stopping criterion of the grid bipartitioning process is also discussed. The proposed bipartitioning strategy is implemented on the IEEE 118-bus system and a practical provincial power grid in China to demonstrate its effectiveness.

A computational algorithm based on biogeography‐based optimization method for computing power system security constrains with multi FACTS devices

AbstractA major role is played by the analysis of power system security in heightening system security and in system collapse condition avoidance. This article presents a cutting edge mechanism which is devised applying transmission line loadings as well as variance in bus voltage magnitude. The use of flexible alternating current transmission systems devices improves the objectives of generation fuel charges in addition to the severity index proposed which were investigated considering the contingency circumstances of generator(s) or/and transmission channel(s). To boost system security in spite of contingency circumstances in the existence of unified power flow controller or UPFC, it would be most appropriate to pinpoint a most advantageous position to install aforementioned device. We propose a model of UPFC where power insertion is done by using voltage source. Also a procedure to incorporate the same and a strategy to find optimum position has been proposed which uses line overload sensitivity indices. This work mainly focused on establishment of available transfer capability on the heavily congested line. The proposed congestion management scheme alleviates the heavy stress in transmission line and provides an ample corridor for the power to flow. Biogeography‐based optimization or BBO in short, is a technique which is a growing recognized optimization method which has been lucratively engaged in solving intricate optimization problem in dissimilar fields. The BBO provides better results than the metaheuristic counter parts such as Genetic Algorithm and Particle Swarm Optimization. The effectiveness of proposed BBO has been tested on standard IEEE 30 bus system and the results are compared with classic methods and other metaheuristic methods. This is established through the MATLAB package. Improved bus voltage profile was also attained and it can be inferred from the outcome that the prospective approach can drastically enhance security of power system when comparing with other optimization methods.

Safety Analysis of Offshore Platform Power System Considering Low Voltage Crossing Capability

In the face of increasingly serious energy and environmental problems, the safety of offshore oil drilling and production platform, as a natural gas development platform, is of great significance. For offshore platforms, the offshore power system serves for oil and gas production, and the interruption or shutdown of oil and gas production caused by electric power is intrinsically unsafe; In addition, due to the weak interconnection between the offshore platform systems, with the characteristics of small unit, large load, wide application of frequency conversion equipment and poor environment, the security analysis of the offshore platform power system must take the power grid and gas network as a whole to analyze and take into account the low-voltage crossing capacity. For the coupling of power grid and gas network of offshore platform, multi-stage compression system and the “power to gas” system are proposed. For these voltage sag sensitive systems, the safe critical voltage and critical cut-off time are used to characterize the low-voltage crossing capability. The operation constraints considering the low-voltage crossing capability are also proposed, and the impact of voltage sag is taken into account in the safety analysis. The simulation results show the correctness of the safety analysis method considering the low voltage crossing capability, and the necessity of the safety analysis considering the gas network and the low crossing capability.

Toward a More Reliable System for Contingency Selection in Static Security Analysis of Electric Power Systems

The reliable supply of electricity plays a key role in the contemporary way of life. In order to provide more benefits to the population, electric networks are getting bigger to produce more energy. This growth, while necessary, brings problems in operation and maintenance, since the networks are more complex. This complexity requires that network security analysis should be performed in real time to avoid decision errors when disconnecting a device from the network or predicting the possibility of operating output from an undersized device. In this article, an intelligent system for contingency selection in the static security analysis of electric power systems is proposed. The severity of contingencies indication is the first step to develop control actions and maintain the system operation integrity. We propose and evaluate the contingency selection as a combinatorial optimization problem, employing an ACO metaheuristic to model the situation. The system is evaluated over the IEEE 30-bus network and a real 810-bus network, considering double outages of branches. Results show an accuracy close to five-nines for severing contingencies.