Synchronised measurements based transient security assessment of power systems using AdaBoost classifiers

Power system security is a major concern in real‐time operation. It is essential to protect the system from blackout by taking proper control actions. This study proposes a boosting algorithm for the precise and accurate prediction of static security assessment of power systems using synchronised measurements. In addition to security status, the proposed approach also predicts the type of violations which may be either line overload/voltage violation or both of the insecure operating conditions. To overcome the computational complexity, the number of input phasor measurements is reduced by a statistical approach based on class separability and correlation coefficient indices. In the classification stage, support vector machines (SVMs) are used as weak classifiers and a strong classifier is constructed as the linear combination of many weak SVM classifiers. The performance of the Adaptive Boosting (AdaBoost) algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. The efficiency of the proposed approach is demonstrated on IEEE 14‐bus, IEEE 30‐bus, and Indian 246‐bus systems. Further, the test results reveal that the proposed method of security assessment performs better than the other traditional classifiers viz. SVM, feed forward neural network and k‐nearest neighbour classifier.

An Efficient Methodology for Study Mode Security Assessment of Power Systems for Large Disturbances

In this paper, a knowledge-based system for static security assessment of power system is proposed and investigated. An expert system (ES) for screening the consequences of transformers' and electrical lines' single outage was developed. The rules derived from a simulation model, irrespective of the size of the power system, are thus obtained. A dedicated artificial intelligence language (Prolog) for rules implementation was used to build the ES named CONTIG. The results show that such ES can be very effective for contingencies' selection. The benefits for a moderate size power system (25 buses and 30 branches) were analyzed using CONTIG. This approach has a great potential for online application

Security assessment and classification are the major concerns in real-time operation of electric power systems. This paper proposes a multiclass support vector machine (SVM) classifier for static and transient security assessment and classification. A straightforward and quick procedure called the sequential forward selection method is used for a feature selection process. The security status of any given operating condition is classified into four modes, viz., secure, critically secure, insecure, and highly insecure, based on the computation of a security index. The proposed SVM-based pattern classifier system is implemented and tested on standard benchmark systems. The simulation results of the multiclass SVM classifier are compared with least-squares, probabilistic neural network, extreme learning machine, and extreme SVM classifiers. The feasibility of implementation of the proposed classifier system for online security evaluation is also discussed.

On-line steady-state security assessment of power systems

Assessment of power system security under load uncertainty

The advantage of fast computation capability of an artificial neural network (ANN) is used to introduce an iterative scheme for security assessment of power systems. Two related approaches are shown which demonstratedly work satisfactorily. The idea of feedback in a single-layer feedforward neural network is experimented yielding higher accuracy. The ANN is trained by using a set of data obtained from off-line analysis of the power network. After training, an approximate solution for a given condition may be found almost immediately. The approximate solution obtained is judged adequate for assessing the security of the power system. A case study is also presented for demonstrating the applicability of the approach. >

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This paper presents the main concepts of small-signal security assessment of power systems, focusing on the online monitoring of small-signal stability. In this work, a computational tool for online monitoring of oscillation modes (OLMO) in power systems is proposed. The objective of the methodology consists of determining small-signal stability margins, security levels, and corrective measures that can improve the damping factor of oscillation modes present in such systems. These corrective measures can be associated with possible redispatches of power plants. In this context, the Closest Security Boundary for Generation Redispatch using Eigenvalue Sensitivities, known as CSBGRES Method, could be applied to determine an optimized redispatch for the power plants of the system to ensure a desired minimum damping factor, previously chosen, for oscillation modes. The proposed tool can be applied for controlling power plant dispatches on Smart Grids, focusing on the damping factor of oscillation modes. A small test system is used to evaluate the proposed computational feature and the results were presented to demonstrate its importance to power system security. Results of the utilization of OLMO on the Brazilian Interconnected Power System (BIPS) are presented to prove the viability of using this method in large-scale power systems.

The progress of the first three tasks of the wind energy part of the contract is discussed. Emphasis is on the modeling of the wind and the detailed computer representations of three candidate wind systems; namely, MOD-1, MOD-2 and the Shackle machine. Once the detailed computer representations of these wind systems have been developed the next step is to begin work on the reduced order models to be used in transient stability programs.

The massive integration of intermittent renewable generation and the increasing variability of demand raise concerns about the high level of uncertainty in the security assessment of power systems. In this context, the main contribution of this study is the proposal of a new definition of contingency criticality, which takes into account the violation probability of the voltage security margin (VSM) while considering correlated uncertainties in both system loads and wind power generation. From this new definition, a contingency ranking can be derived and used to determine preventive control actions. To calculate this probability for each contingency, a new approach based on the cross-entropy (CE) method is developed and applied. The CE method is well-suited to handle high levels of uncertainty, as it typically provides faster and more accurate results compared to Monte Carlo simulation, particularly for cases with low violation probabilities of the VSM. Another innovative feature of this approach is the consideration of correlated uncertainties through the use of multivariate normal distributions and Gaussian copulas. Furthermore, the proposed definition is implemented using a formulation that is capable of detecting either saddle-node or limit-induced bifurcations to accurately identify the maximum loadability point. A proof of concept is presented for a comprehensive explanation of the proposed definition, followed by an application of this definition to the IEEE 118-bus test system. The findings of this paper highlight the need to carefully select critical contingencies for voltage security assessment in the context of increasing uncertainties.

The progressive development in power systems and their operational complexity has aroused interest in the power system dynamic security. Network operators cannot determine the exact state of the system in high-risk situations causing the spread of failures in the system and breakdowns in the worst cases. According to this, the current paper presents the development of a user interface made for the dynamic security assessment of power systems using the theory of situational awareness (SA) to improve the visualization and analysis of the network operator’s information in control centers. Likewise, this paper proposes a methodology, based on measurable information in a WAMS environment, to identify and classify the disturbance or phenomenon affecting the safety of the power system by using online identification of coherent groups, SA indexes and fuzzy logic. Significantly, the purpose of this document does not include control actions when a contingency occurs. It is noteworthy that the validation of this methodology is performed by simulations in the power system New England Test System (39 nodes) by using PSAT toolbox of Matlab®.

95/00501 On-line steady-state security assessment of power systems

Power system operation is increasingly dependent on the intermittent behavior of renewable energy sources. It is well known, that weather forecasts become more exact the shorter they look into the future. Hence, actual power system states differ considerably from the day ahead planning. To ensure the secure operation under these circumstances, the much more exact forecasts of 1 to 4 hours ahead must be used to determine the system security and enable sophisticated measures to be taken to prevent the system from suffering events like blackouts.

In power system security assessment, security indices or approximate system performance (ASP) models are used to predict the system security. The authors develop performance curves based on probabilistic methods which indicate the effectiveness of the various security indices used in steady state security assessment. These curves give the probability of detecting insecurity as a function of false alarm probability, thus showing the tradeoff obtained when a particular security criterion is selected. These performance curves are used to illustrate the change in the effectiveness of the indices as the operating scenario and the uncertainties change. A 14 bus system is used to illustrate the probabilistic evaluation of the three scalar indices for predicting thermal limit violations. >