The Growing Hierarchical Self-Organizing Feature Maps And Genetic Algorithms for Large Scale Power System Security

Segmentation of gray level images into regions of uniform texture is investigated. An unsupervised approach through the use of Kohonen's self organizing map (SOM) and a multilayer version of it, the hierarchical self organizing map (HSOM), is employed to find the regions in an image composed of textures from different classes. For testing, gray level artificial textured images modeled as Markov random fields are used as the input. No parameter estimation is done. The size and the topology of SOM and HSOM are independent from the size of the input image. The segmentation results are very promising.

State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

Guest Editorial: Situational awareness of integrated energy systems

According to the contradiction between the static power system state estimation and the actual operation of power system, based on the study of the classical algorithms of power system state estimation, apply the gray system theory of power system based on GM (1, 1) mode to the state estimation of power system, and create a new dynamic power system state estimation algorithm, for example to a four-node power system network test for State estimation simulation. Measurement of a known algorithm used to predict the gray system theory. On the other hand, measurement of unknown quantity, using the result that come from the known quantity, then get all parameters with character of power system state through statistical analysis of simulation results that the normal measurement system, the method is feasible and has a good filtering effect. The power system state estimation algorithm based on the gray system theory of Power System based on GM (1, 1) model has high estimating precision, fast calculation, use small memory and the adaptability of the measurement system is very strong, so it has good prospects.

Cuticular hydrocarbons were extracted from workers of 63 different nests of five species of Tetramorium ants (Hymenoptera: Formicidae) from Austria, Hungary, and Spain. The GC-MS data were classified (data mining) by self-organizing maps (SOM). SOM neurons derived from primary neuron separation were subjected to hierarchical SOM (HSOM) and were grouped to neuron areas on the basis of vicinity in the hexagonal output grid. While primary neuron separation and HSOM resulted in classifications on a level more sensitive than species differences, neuron areas resulted in chemical phenotypes apparently of the order of species. These chemical phenotypes have implications for systematics: while the chemical phenotypes for T. ferox and T. moravicum correspond to morphological determination, in T. caespitum and T. impurum a total of six chemical phenotypes is found. Three hypotheses are discussed to explain this disparity between morphological and chemical classifications, including in particular the possibility of hybridization and the existence of cryptic species. Overall, the GC-MS profiles classified by SOM prove to be a practical alternative to morphological determination (T. ferox, T. moravicum) and indicate the need to revisit systematics (T. caespitum, T. impurum).

Power system state estimation is one of the most important functions of power system control centers. In recent years, the complexity of power system state estimation has significantly increased due to the growing number of distributed, including renewable energy sources, electric vehicles, the demand response technologies, and the increased risk of cyber-attacks. Under these conditions, state estimation methods, which consider information about the time-correlation of the power system states have a great potential. The correlation is described by a transition model. The well-known state estimation methods usually use one single model. However, in case of stochastic behavior of the load and generation, it is impossible to assert the adequacy of the chosen model over the entire observation interval. Therefore, the paper proposes a multi-model forecasting power system state estimation method, which has lower errors in comparison with a single-model assessment at the moments of the lowest accuracy of the latter. Multi-model parameter estimation is used based on three procedures of the single-model Kalman filtering estimation and various transition models based on autoregressive and vector autoregressive analyzes, as well as Holt's exponential smoothing. Uniting the single-model estimation has been carried out according to the criterion of the minimum of variance of the resulting estimate. An algorithm of multi-model power system state estimation has been developed. Its version has been analyzed using a three-model forecasting-aided estimation using linear transition models. The state of the IEEE 30-bus test power system has been assessed by the means of simulation modeling. The maximum accuracy increase of the multi-model estimation in comparison with the single model is set up. 26,1 % is for autoregressive analysis; 16,9 % is for vector regression analysis and 37,7 % is for Holt’s exponential smoothing. The proposed method of multi-model state estimation has higher robustness and accuracy at the moments of the lowest accuracy of single-model estimation. It is advisable to use the method to solve control tasks of power systems with rapidly changing dynamic modes.

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.

Data concerning actual temperatures of line conductors constitutes essential information for the power system operator. The temperature of the power lines can be used to improve the accuracy of the power system model, thereby increasing the accuracy of the state estimation. This article presents a two-step algorithm for the power system state and line temperature estimation. In its second stage, the proposed method searches for a line temperatures vector, which corrects the uncertain power system base model and allows for further minimization of an objective function. As a result, a more accurate estimation is obtained along with a more precise model of the estimated system. The derived model can then be used for more accurate optimization. The presented method enhances standard procedures of power system state estimation, and its advantage is that it does not require direct measurements performed by phasor measurement units or measurements of line conductor temperatures and weather conditions realized by dynamic line rating systems. The results of simulations made on various test models have been examined, confirming the convergence of the procedure to the point at which the average temperature of the line wires together with the voltage values and phase angles are achieved. The algorithm’s performance and improvement method have also been presented. An advantage of the investigated approach is the possibility to calculate the temperature of line wires with the use of primary measurements in the power system. The presented and examined method, however, is sensitive to the measuring device errors. Additionally, an analysis of the method’s errors and ways of reducing them has been performed.

The demand of quality & reliable power by the consumers is increasing day by day in a tremendous manner. Everyone needs good quality of power with out any disturbances or interruptions. Also due to large no of present day consumers, the power system needs a boosting mechanism in order to capitalize the complexity of power system. So a reliable technology of FACTS(Flexible AC Transmission System) is introduced in the power system to enhance the flexibility of power grids. FACTS devices are power electronics based controllers & improve the system performance by shunt or series compensation. They injects or absorbs reactive power to/from the power system at the time of need. In this paper a shunt type of FACTS controller such as SVC(Static Var Compensator) is considered for stability enhancement & loss minimization in a nine bus power system. SVC consists of TCR(Thyristor Controlled Reactor), TSC(Thyristor Switched Capacitor), a fixed capacitor connected parallel to the line & filter elements for harmonic reduction. Here the enhancement of voltage stability & loss minimization is analyzed in terms of voltage magnitude variation & overall real & reactive power losses at each bus w.r.t the location of installation of SVC. Firstly Load flow study carried out for an uncompensated system & then for SVC compensated system by changing the location of installation of SVC from bus1 to bus9. And also thereby specifying the optimal location of SVC by analyzing the load flow data in terms of voltage magnitude variation & overall losses. All the performance analysis has been performed using MATLAB/SIMULINK software.

Recently, owing to the high penetration of renewable power generations, random access to dynamical loads and wide application of power-electronic devices, the modern power systems exhibit new features such as high dynamics, low inertia and weak voltage and frequency support. As a powerful tool in revealing the dynamical evolution of the modern power systems, the dynamic state estimation (DSE) scheme gains a surge of research enthusiasms. Note that in actual power systems, various styles of power generations and mixed measurements generated by the supervisory control and data acquisition (SCADA) system and the phasor measurement unit (PMU) pose higher demands on the DSE. In addition, the measurements with anomalies such as gross errors, outliers and incomplete information are often encountered due to the unreliable measurement facilities, constrained communication resources and cyber-threats. Such anomalies, if not tactfully handled, would largely impair the DSE performance. In this paper, a systematic and timely review with respect to DSE for power systems is provided. First, several typical models for power systems include quasi-steady model, synchronous-generator-based multi-machine model and renewable power generation model are introduced. Then the measurement facilities of power systems and their features as well as the measurements with anomalies are reviewed, respectively. Subsequently, the DSE schemes are recalled and discussed from the perspectives of framework, methodology, anomaly-resistant ability and application, respectively. Finally, some possible future research topics are outlined for the DSE of power systems.

This paper presents an algorithm for state estimation in power systems that include FACTS devices and PMUs. The FACTS devices are equipments of special purpose capable of changing the natural behavior of transmission systems. They may be able to influence the voltage, the active power or the reactive power flows based on predefined targets so that the classical power flow and steady state calculations must be adapted. Furthermore, PMU measurements are increasing in number so that the accuracy in steady state calculations can be improved. A state estimation algorithm was developed by considering the behavior of TCSC and SVC devices, while including some improvements due to the integration of synchronized measurements. A Matlab application was developed and simulations were performed on various test networks. In this paper, results obtained on the IEEE 30 bus test system only will be presented.

Robust adaptive Unscented Kalman Filter with gross error detection and identification for power system forecasting-aided state estimation

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

Yenilenebilir enerji kaynaklarının hızlı gelişimi, dağıtık üretim tesislerinin entegrasyonu ve dijitalleşmedeki ilerlemeler dünya genelindeki güç sistemlerinde karmaşık bir değişime neden olmaktadır. Güç sistemlerinin karmaşıklığı hem lisans hem de lisansüstü öğrencilerinin güç sistemleri ile ilgili çalışmalarını sıkıcı, zor ve zaman alıcı hale getirmektedir. Bununla birlikte, simülasyon araçlarının kullanılması, güç sistemleri çalışmalarıyla ilgili hesaplama işlemlerini büyük ölçüde basitleştirmiştir. Yalnızca güç sisteminin mevcut çalışma koşulları altında çalışmasını değil, aynı zamanda gelecekteki genişlemelerin planını ve tasarımını da değerlendirmek için güç akışı çalışmaları giderek daha gerekli hale gelmektedir. Bu makale, öğrencilerin güç sistemlerindeki karmaşık olayları daha iyi anlamalarına yardımcı olmak için gelişmiş görselleştirme teknikleri kullanan PowerWorld Simülatör ile güç akışı analizinin bir incelemesini sunmaktadır. Bu amaçla, Stevenson’ın 5 baralı güç sisteminin güç akışı analizi Newton-Raphson, Gauss-Seidel ve Hızlı Ayrık metotları ile incelenmiştir. Simülasyon sonuçları hem görsel olarak hem de tablo olarak verilmiştir. Sonuç olarak, PowerWorld Simülatör ile öğrencilerin geleneksel çözümlere ek olarak güç sistemi analiz problemlerini daha iyi anlayacağı görülmüştür.

The widespread deployment of Phasor Measurement Units (PMUs) on power systems has facilitated the real-time monitoring of the power systems dynamics. Dynamic State Estimators (DSEs) are used by the investigators to estimate and identify the state variables and parameters of the nonlinear dynamic models within the power systems by using the measurements which are mainly provided by the PMUs. This dissertation addresses fundamental research on dynamic state estimation of the power systems and presents innovative and robust dynamic state estimation approaches for estimation/identification of the state variables/parameters associated with the nonlinear dynamic models within the power systems.