Active Power Performance Index Research Articles

Development and Application of Probabilistic Performance Index for Ranking N-1 Contingencies

Contingency analysis and ranking are important tasks in modern electrical power systems which aim at keeping the power system secure, reliable, and stable. N-1 contingency is the loss of any one component of power system and is obviously the most frequent contingency in power system. Contingency ranking has most often been done using deterministic indices which can be either active power performance index (PIP), voltage performance index (PIV) or the overall performance index (PI). Power system contingencies are ranked based on the calculated Performance index for each contingency. Ranking is from the contingency with the highest performance index first and proceeds in a descending manner which corresponds to the most severe to the least severe contingency. Due to the fact that Contingencies are unpredictable events, researchers of recent have suggested the inclusion of the probability of the occurrence of a contingency in its ranking index. This makes the index probabilistic. In this work, the development and application of probabilistic performance index for ranking N-1 contingencies is considered. It is illustrated with a case study.

Development and Application of Probabilistic Performance Index for Ranking N-1 Contingencies

Contingency analysis and ranking are important tasks in modern electrical power systems which aim at keeping the power system secure, reliable, and stable. N-1 contingency is the loss of any one component of power system and is obviously the most frequent contingency in power system. Contingency ranking has most often been done using deterministic indices which can be either active power performance index (PIP), voltage performance index (PIV) or the overall performance index (PI). Power system contingencies are ranked based on the calculated Performance index for each contingency. Ranking is from the contingency with the highest performance index first and proceeds in a descending manner which corresponds to the most severe to the least severe contingency. Due to the fact that Contingencies are unpredictable events, researchers of recent have suggested the inclusion of the probability of the occurrence of a contingency in its ranking index. This makes the index probabilistic. In this work, the development and application of probabilistic performance index for ranking N-1 contingencies is considered. It is illustrated with a case study.

Power System Contingency Analysis by using Voltage and Active Power Performance Index

Power System Contingency Analysis by using Voltage and Active Power Performance Index

Principle Component Analysis Based Data Mining for Contingency Analysis on IEEE 30 Bus Power System

This Paper is an attempt to develop a Data Mining tool for the contingency of the power system. By mining the big data in the power system and analyzing the early detection of the contingency in the power system a larger cost cutting can be planned. As Mining would reduce the computational complexity of the contingency analysis this attempt would lead to reduction in the hardware use. This paper uses Multiclass Relevance Vector Machine(MCRVM) and Multiclass Support vector machine(MCSVM) in order to mine the data which include the voltage, power generated , power angles , power demand in different lines of the power system. The Data mining would need a data transformation technique, which would reduce the dimensionality of the data introduced for mining. The combination of Data cleansing and the Principal Component Analysis would act as the data transformation technique in this paper. A Matlab based simulation is carried using the IEEE 30 bus system for the contingency analysis by incorporating the loading risk assessment strategy using the Multiclass SVM and RVM and the results are compared and the outputs are tabulated. Active power performance index and the reactive power performance index are used in contingency analysis of the IEEE 30 bus system thus used and the accuracy of classification and the speed of classification with the different methods and the contingency rankings are found and displayed.

Identification of system vulnerabilities in the Ethiopian electric power system

The Ethiopian Electric Power (EEP) has been operating and managing the national interconnected power system with dispersed and geographically isolated generators, a complex transmission system and loads. In recent years, with an increasing load demand due to rural electrification and industrialization, the Ethiopian power system has faced more frequent, widely spread and long lasting blackouts. To slash the occurrence of such incidents, identifying the system vulnerabilities is the first step in this direction. In this paper, the vulnerability assessment is performed using indices called active power performance index (PIp) and voltage performance index (PIv). These indices provide a direct means of comparing the relative severity of the different line outages on the system loads and voltage profiles. Accordingly, it is found that the most severe line outages are those lines that interconnect the high load centered (Addis Ababa and Central regions) with the rest of the regional power systems. In addition, the most vulnerable buses of the network in respect of voltage limit violations are mainly found at the high load centers.

An online power system static security assessment module using multi-layer perceptron and radial basis function network

Abstract Efficient contingency screening and ranking method has gained importance in modern power systems for its secure operation. This paper proposes two artificial neural networks namely multi-layer feed forward neural network (MFNN) and radial basis function network (RBFN) to realize the online power system static security assessment (PSSSA) module. To assess the severity of the system, two indices have been used, namely active power performance index and voltage performance index, which are computed using Newton–Raphson load flow (NRLF) analysis for variable loading conditions under N − 1 line outage contingencies. The proposed MFNN and RBFN models based PSSSA module, are fed with power system operating states, load conditions and N − 1 line outage contingencies as input features to train the neural network models, to predict the performance indices for unseen network conditions and rank them in descending order based on performance indices for security assessment. The proposed approaches are tested on standard IEEE 30-bus test system, where the simulation results prove its performance and robustness for power system static security assessment. The comparison of severity obtained by the neural network models and the NRLF analysis in terms of time and accuracy, signifies that the proposed model is quick, accurate and robust for power system static security evaluation for unseen network conditions. Thus, the proposed PSSSA module implemented using MFNN and RBFN models are found to be feasible for online implementation.

Effect of line contingency on static voltage stability and maximum loadability in large multi bus power system

Abstract Voltage instability is the phenomena associated with heavily loaded power systems. It is normally aggravated due to large disturbance. Due to deregulation of power system, the system has to cater for the load bids in open access. This may cause the increased transaction level leading to more stress on the power system. It has become an urgent need of today to address the voltage stability problems to keep the voltage profile under control. Several incidences of voltage instability have occurred worldwide recently. In the event of contingency, the most serious threat to operation and control of power system is insecurity. The estimation of the power system state under contingency is an essential task for the power system engineers. The contingency analysis technique is a prerequisite to predict the effects of various contingencies like failure of transformers, transmission lines, etc. It helps to initiate necessary control actions to maintain power system security, reliability and stability. In the present work, two kinds of performance indices viz. active power performance index and reactive power performance index are computed for a large power system using a special code written in MATLAB environment. Furthermore, a novel idea of static voltage stability analysis by incrementing the load in proportion to the original load on load buses along with the most vulnerable line outage using Newton Raphson (N–R) load flow is presented.

Contingency screening for steady-state security analysis by using FFT and artificial neural networks

A new approach based on artificial neural networks (ANNs) combined with fast Fourier transform (FFT) is developed for single line contingency screening in steady-state security analysis. The offline fast decoupled load flow calculations are adopted to construct two kinds of performance indices, PI/sub p/ (active power performance index) and PI/sub v/ (reactive power performance index) which reflect the severity degree of contingencies. The results from offline calculations of the load flow are used to train a multilayered artificial neural network for estimating the performance indices. FFT is used for preprocessing the inputs to improve and speed up the ANN training. The effectiveness of the proposed method is demonstrated by contingency ranking on two IEEE test systems and comparisons are made with the traditional method. Good calculation accuracy, high contingency capturing rate and faster analysis times for contingency screening are obtained by using the ANNs.

A Fast Loop-Based Algorithm for Automatic Contingency Selection

In order to speed up on-line security assessment, a fast loop-based automatic contingency selection algorithm is presented in this paper. A linearized active power flow model is built up and loop analysis method with introduction of series potential compensation is used to compute the distribution of the flows after occurance of outages, thereby the change of active power performance index due to outage is calculated, and from which an outage list is constructed. Instead of using detailed AC (or DC) load flow calculation for each outage case, only one m-dimensiona1 forward/backward substitution solution is required to construct the outage list, where m is the number of loops which is much less than the number of buses. Therefore, this method is very fast in speed in contrast with the node-based method. Several digital examples are carried out and the calculated results show that the proposed method is more accurate than that by using a single P-Q iteration of FDLF, at the same time, the calculation speed is hundreds of times faster than the latter. Simultaneous multi-outages can also be simulated and their formulas for calculation are also given.