Security Of Electric Power System Research Articles

A robust crow search algorithm based power system state estimation

The State Estimation (SE) computational procedure plays a crucial role in modern electric power system security control by monitoring and analyzing operational conditions and predicting any emergency. In order to estimate state variables, Power System State Estimation (PSSE) takes into account the magnitudes and phases of voltage on each bus. To address the state estimation challenges in power systems, in this paper, we propose a novel application of the Crow Search Algorithm (CSA) specifically tailored for the state estimation problem. We have assessed the introduced algorithm using the frameworks of both the IEEE 14-bus and IEEE 30-bus test systems. The first formulation is the Weighted Least Square (WLS) method, and the second is the Weighted Least Absolute Value (WLAV) method, both of which are objective function formulations. By comparing the results, it is clear that CSA-based SE is superior to the other metaheuristic algorithms considered, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Swarm Optimization (ABSO). As a point of comparison, we use the Newton–Raphson method for calculating load flow. It has been shown that the proposed CSA-based SE technique has better accuracy than the other two algorithms in all different test systems. With this study, the power system is operated more accurately and reliably by the operators operating the system.

Pemanfaatan Simulasi Monte Carlo Untuk Perhitungan Kinerja Gangguan Tenaga Listrik Pada PT PLN (Persero) UIW Suluttenggo

Objective: This study aims to determine the optimum number of electrical disturbances at PLN UIW Suluttenggo both in terms of frequency and duration of disruption to determine the level of quality of electric power reliability service on the customer side. Quality reliability or "reliability" is related to the ability of a system to deliver electricity to all points of its user in the appropriate standards and quantities. There are two things related to the reliability of electric power (1) Adequacy (adequacy) and (2) System security (security).
 Method: Monte Carlo simulation is a simulation model that includes a series of random sampling with a probability distribution that can be known and determined, so this simulation can be used. In this study, data was taken from the number of electrical disturbances that occurred at PLN UIW Suluttenggo in 7 service unit branches using SAIDI and SAIFI data in 2021 and 2022.
 Findings: The finding of this study is that the use of Monte Carlo simulations can be used to analyze ganggaun data where the calculation results compared to the calculation of the excell formula produce almost the same results.
 Results: The results of the interference performance calculation showed the best performance were UP3 Manado (1.65), UP3 Kotamobagu (1.55) and UP3 Gorontalo (1.05). As for each interruption performance for the shortest duration of outage is UP3 Kotamobagu (0.75) and the smallest interference frequency is UP3 Manado (1.0).

Novel approach for relays tuning using detailed mathematical model of electric power system

The correct operation of relay protection (RP) devices largely determines the security of electric power systems (EPS). The key point that in turn determines the behavior of protection in various emergency modes is a determination of their settings. The existing methodologies and tools often do not allow to provide RP settings adequate to the practical conditions of their functioning. This statement is confirmed by the statistics of faults in EPSs. The main reason for this issue is the inability to reliably reproduce transients in EPS via the software programs used in practice to calculate the RP settings. The Hybrid Real-Time Power System Simulator (HRTSim), developed by the authors, allows to reliably reproduce the entire range of normal and emergency modes and processes in EPS of any scale, topology and configuration by using detailed three-phase models of all power system components. Taking into account this possibility and features of the HRTSim, the task of detailed modeling of RP devices, including instrument current and voltage transformers, becomes relevant. The implemented special tools for RP modeling and the HRTSim in general allow to develop a new methodology for RP tuning. At the same time, the typical elements – relays – can be identified for each type of RP that determine their operation algorithm. This paper presents an analysis of the main relays of the most modern microprocessor-based RP devices and proposed principles of their tuning. The developed approach allows to adapt the RP settings to the practical conditions of their functioning in EPSs and to minimize the probability of RP maloperation.

Development of an Ensemble Decision Tree-Based Power System Dynamic Security State Predictor

Real-time assessment and prediction of dynamic security plays an important role in power systems operation and control. Analysis of the current operating state and prediction of plausible future insecurity is crucial for real-time decision-making and implementation of necessary control strategies to ensure secure operation of bulk power systems. On-line security assessment demands low complexity and computational time. The traditional methods of assessment of dynamic security of electric power systems often prove to be inadequate for online and real-time conditions due to their high complexity and long computation time. This article proposes a pattern recognition approach to dynamic security analysis for large and complex power systems using ensemble decision tree to form reliable decision rules for fast and accurate prediction of power system's operating states in a real-time and on-line environment. An ensemble security predictor (ENSP) was developed and trained to act as “dynamic security state predictor” to predict and classify power system's dynamic operating states into secure, insecure, and intermediate transitional classes. Two different case studies were performed on IEEE 118-bus and IEEE 300-bus systems to evaluate and compare the performance of the proposed method on systems of varying size and complexity. While prediction accuracy of some of the single-learner classifiers decreased considerably when implemented on a large sized system, the performance of ENSP declined marginally, and the results were acceptable for very large sized systems.

Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

Influence of moment of inertia on electric power system security and stability control

The moment of inertia is the basic parameter of the generator and also the basic data of PSS parameter tuning. The parameter setting of PSS will increase the damping of the unit and improve the small disturbance stability of the system. When the rotational inertia parameter is not consistent with the real rotational inertia of the unit, the PSS parameter setting will not increase the damping of the unit, but sometimes it will cause the oscillation of the unit. Here, the correlation analysis is carried out with the small signal stability analysis program, and the field test is carried out in Qinghai Kang Yang power plant. The influence of moment of inertia on PSS is analysed by small disturbance analysis and field test.

A Predictive Model for Dry-Growth Icing on Composite Insulators under Natural Conditions

Icing can adversely influence electric power system security. Two main issues are caused by icing: the overload of transmission lines, and the reduction in the insulation ability of the insulators. Most previous research has focused on the flashover characteristics of ice-covered insulators, but research on the icing process of the insulator is seriously lacking. Considering the effect of icing shape, the outer airflow field of an insulator was calculated and the local collision efficiencies of water droplets (β1) were investigated according to the Lagrange algorithm. The simulation showed that the values of β1 on the insulator edge and rod are much higher than on the insulator surface, and both were significantly influenced by the wind speed and median volume diameter (MVD) of the water droplets. Based on thermal balance equations, a dynamic dry-growth icing model was established. Using the natural icing conditions of Xuefeng Mountain (China) as an example, validation experiments were conducted on a composite insulator and the climate parameters measured by multi-cylinders were used to model the icing shape and mass. The results indicate that high wind speed and low temperature increase icing rate; the icing was mainly concentrated on the windward side and the greatest horizontal thickness was generally on the insulator edge. The dry-growth model had an average error lower than 25% for icing thickness and an average error lower than 20% for icing mass, which were affected by icing roughness.

A Suite of Intelligent Tools for Early Detection and Prevention of Blackouts in Power Interconnections

We propose a suite of intelligent tools based on the integration of methods of agent modeling and machine learning for the improvement of protection systems and emergency automatics. We propose an online approach to the assessment and management of dynamic security of electric power systems (EPS) with the use of a streaming modification of the random forest algorithm. The suite allows to recognize dangerous modes of complex closed-loop EPS, preventing the risk of emergencies on early stages. We show results of experimental tests on IEEE test systems.

Power management system implementation on off-shore gas platforms

Electrical power production system of Ivana A&K platforms consists four three-phase generators driven by gas motors. Besides, there are two emergency diesel generator sets for emergency power supply. Generators are feeding power motor control centre board and essential user board on each platform. Due to the reliability, security, efficiency and stability of this islanded industrial electric power system, it was decided to implement a comprehensive power management system which should provide load-shedding solution and protect the plant against blackouts and power source outages due to system disturbances. Selected system consists of a power management intelligent electronic device with remote I/O and measuring devices deployed throughout the power system. System relies on IEC 61850 GOOSE communication for fast and accurate data exchange. In this study, the process of the system selection and implementation has presented and described according to the conducted procedure.

Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration

This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs); EVs interact with the grid through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services to ensure reliable and cost-effective grid operation. This research provides a computational framework for this decision-making process. Charging and discharging strategies of EV aggregators are incorporated into a security-constrained optimal power flow (SCOPF) problem such that overall energy cost is minimized and operation within acceptable reliability criteria is ensured. Particularly, this SCOPF problem has been formulated for Jeju Island in South Korea, in order to lower carbon emissions toward a zero-carbon island by, for example, integrating large-scale renewable energy and EVs. On top of conventional constraints on the generators and line flows, a unique constraint on the system inertia constant, interpreted as the minimum synchronous generation, is considered to ensure grid security at high renewable penetration. The available energy constraint of the participating EV associated with the state-of-charge (SOC) of the battery and market price-responsive behavior of the EV aggregators are also explored. Case studies for the Jeju electric power system in 2030 under various operational scenarios demonstrate the effectiveness of the proposed method and improved operational flexibility via controllable EVs.

Using of sensitivity models of the steady-state of electric power systems for account of failure of elements

The article presents an algorithm for obtaining sensitivity models of the first and second orders of the steady-state regime of electric power systems (EPS). The sensitivity models are intended for express calculations of steady-state when estimating the static security of EPS. The use of sensitivity models allows one to simulate failures of EPS elements without calculating new steady-state. To verify the reliability of the sensitivity models obtained, the results of an experiment performed on a 3-node test pattern are presented.

Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method

Abstract The Critical Clearing Time (CCT) is a key issue for Transient Stability Assessment (TSA) in electrical power system operation, security, and maintenance. However, there are some difficulties in obtaining the CCT, which include the accuracy, fast computation, and robustness for TSA online. Therefore, obtaining the CCT is still an interesting topic for investigation. This paper proposes a new technique for obtaining CCT based on numerical calculations and artificial intelligence techniques. First, the CCT is calculated by the critical trajectory method based on critical generation. Second, the CCT is learned by Extreme Learning Machine (ELM). This proposed method has the ability to obtain the CCT with load changes, different fault occurrences, accuracy, and fast computation, and considering the controller. This proposed method is tested by the IEEE 3-machine 9-bus system and Java-Bali 500 kV 54-machine 25-bus system. The proposed method can provide accurate CCTs with an average error of 0.33% for the Neural Network (NN) method and an average error of 0.06% for the ELM method. The simulation result also shows that this method is a robust algorithm that can address several load changes and different locations of faults occurring. There are 29 load changes used to obtain the CCT, with 20 load changes included for the training process and 9 load changes not included.

Application of Flower‐Like ZnO Nanorods Gas Sensor Detecting SF6 Decomposition Products

Gas insulated switchgear (GIS) is an important electric power equipment in a substation, and its running state has a significant relationship with stability, security, and reliability of the whole electric power system. Detecting and analyzing the decomposition byproducts of sulfur hexafluoride gas (SF6) is an effective method for GIS state assessment and fault diagnosis. This paper proposes a novel gas sensor based on flower‐like ZnO nanorods to detect typical SF6 decompositions. Flower‐like ZnO nanoparticles were synthesized via a simple hydrothermal method and characterized by X‐ray powder diffraction and field‐emission scanning electron microscopy, respectively. The gas sensor was fabricated with a planar‐type structure and applied to detect SF6 decomposition products. It shows excellent sensing properties to SO2, SOF2, and SO2F2 with rapid response and recovery time and long‐term stability and repeatability. Moreover, the sensor shows a remarkable discrimination among SO2, SOF2, and SO2F2 with high linearity, which makes the prepared sensor a good candidate and a wide application prospect detecting SF6 decomposition products in the future.

Security-constrained transmission switching with voltage constraints

Transmission switching (TS) would play a vital role in the security and economics of electric power systems. The application of TS to the AC model of security-constrained unit commitment (SCUC) for the day-ahead scheduling is presented in this paper. The proposed AC model of SCUC with TS would include real and reactive power flow constraints which increase the controllability of base case and contingency solutions with voltage constraints. A general FACTS model is introduced for the reactive power management in SCUC which is based on the power injection model (PIM). A modified Newton–Raphson power flow model is introduced in the proposed SCUC with TS in which line flows are considered as variables. The proposed AC network model is compared with the DC network model for enhancing the power network controllability and minimizing the operation cost. The case studies exhibit the effectiveness of the TS application to SCUC with AC network constraints.

Importance Sampling of Injected Powers for Electric Power System Security Analysis

Power system security analysis is often strongly tied with contingency analysis. To improve Monte Carlo simulation, many different contingency selection techniques have been proposed in the literature. However, with the introduction of more variable generation sources such as wind power and due to fast changing loads, power system security analysis will also have to incorporate sudden changes in injected powers that are not due to generation outages. In this paper, we use importance sampling for injected-power simulation to estimate the probability of system failure given a power system grid state. A comparison to standard crude Monte Carlo simulation is also performed in a numerical example and indicates a major increase in simulation efficiency when using the importance sampling technique proposed in the paper.

DAILY ARTIFICIAL DISPATCHER – THE OTHER HEAD

The paper suggests Daily Artificial Dispatcher (DAD), as an ambient intelligence that solves the problem for automatic predictive analysis of electric power system (EPS) security and efficiency, using EPS and weather summarized data. DAD is constructed as heading EPS, implementing common sense thinking a day ahead for EPS. DAD is verified, as it is shown that it is an intentional agent, a robot, and an intelligent system. DAD is validated, as it is shown that it is an EPS adequate security and efficiency computation.

Security Constrained Economic Dispatch

The main objective of electric power dispatch is to provide electricity to the customers at low cost and high reliability. Transmission line failures constitute a great threat to the electric power system security. We use a Markov decision process (MDP) approach to model the sequential dispatch decision making process where demand level and transmission line availability change from hour to hour. The action space is defined by the electricity network constraints. Risk of the power system is the loss of transmission lines, which could cause involuntary load shedding or cascading failures. The objective of the model is to minimize the expected long-term discounted cost (including generation, load shedding, and cascading failure costs). Policy iteration can be used to solve this model. At the policy improvement step, a stochastic mixed integer linear program is solved to obtain the optimal action. We use a PJM network example to demonstrate the effectiveness of our approach.

Short-term generation scheduling of thermal units with voltage security and environmental constraints

A dynamic and linear programming-based approach is investigated for the short-term scheduling of thermal power plant units considering the security and environmental constraints of electric power systems. In addition to the basic constraints (i.e. real power limits of the generators, real power demand, spinning reserve, minimum up and down lime of units) a comprehensive set of constraints is considered including: transmission line capacity limits; generators dynamic ramp rate; voltage security margin; and environmental issues. An optimal multiplier continuation method is adopted to effectively calculate the voltage security margin. The constraint relating to the voltage security margin is considered as a transition constraint in the dynamic programming process. The environmental issue is considered as a restriction on the hourly SO/sub 2/ emission allowances and other constraints are considered in the dynamic/linear programming procedures. Results of the application of the proposed algorithm to a test system are presented to illustrate the effectiveness of the approach.

Neural networks' power

Neural networks (NNs) are effective systems for learning pattern discriminants from a body of examples. Artificial neural networks (ANNs) have been developed in a wide variety of configurations with some common underlying characteristics. All ANNs attempt to achieve good performance via massive interconnection of simple computational elements. Neural networks are characterized by the model of their neurons, the connections among them and the methods used to train them to do specific tasks. The author describes multi-layer neural networks and Kohonen neural networks. The author then discusses how they are used in electric load forecasting and power system security assessment.

A neural network-based method for voltage security monitoring

In this paper, a neural network-based method is proposed for monitoring the online voltage security of electric power systems. Using a dynamic model of the system, voltage stability is measured totally, considering a suitable stability index for the whole system, and locally, by defining appropriate voltage-margins for detecting the area of the system where the instability phenomenon arises. A three-layer feedforward neural network is trained to give, as outputs to a pre-defined set of input variables, the expected values of the above defined indices. The neural network is designed by using a fast learning strategy that allows the optimal number of hidden neurons to be easily determined. Moreover, it is shown that, in the operation mode, the system power-margin and the bus power-margins can be easily evaluated using the value of the voltage stability index given by the designed NN. The effectiveness of the proposed approach has been demonstrated on the IEEE 118-bus test system.