Contingency analysis is a tool used by power system engineers for planning and assessing
power system reliability. The conventional analytical method which is mathematical model based,
is not only tedious and time consuming in view of the large number of components in the network
but always left some critical components unassessed due to non-convergence of the power flow
analysis of such, hence the contingency analysis of such system could not be said to be
completed.
In this work, contingency analysis of line components of a standard IEEE-30 Bus and real 330-kV
Nigerian Transmission Company of Nigeria (TCN) network (28Bus) systems were investigated
using Radial Basis Function Neural Network (RBF-NN) which is artificial intelligence based.
The contingency analysis was carried out by solving the non-linear algebraic equations of steady
state model for the standard IEEE-30 Bus and TCN-28 Bus power networks using NewtonRaphson
(N-R) power flow method. RBF-NN method was used for the computation of Reactive
and Active performance indices (PIR and PIA ) which were ranked in order to reveal the criticality
of each line outage. Simulation was carried out using MATLAB R2013a version. The nonconverged
lines in both systems were reinforced and re-analysed. The results of contingency
analyses of the reinforced systems show more robust systems with complete line ranking.
In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient training methods called hybrid learning method.The method requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.
Neural wavelet based hybrid model for short-term load forecastingAlexander Decker
This document summarizes a research paper that proposes a neural-wavelet based hybrid model for short-term load forecasting. The paper introduces neural networks and how they can be used for electric load forecasting. It then proposes a model that uses wavelet transforms for preprocessing the original load signal data into different levels, before inputting these into a neural network for short-term load forecasting. The model is tested and results show the neural-wavelet model provides more accurate forecasts than an artificial neural network alone.
The document proposes an artificial bee colony (ABC) algorithm based neuro fuzzy controller (NFC) to improve the performance of a unified power quality conditioner (UPQC) in compensating for power quality issues like voltage sags. The NFC uses the error and change in error voltage as inputs to a neural network. The ABC algorithm is used to optimize the neural network output. This optimized output is then used to generate optimal fuzzy rules and calculate the discharging capacitor voltage from a bias voltage generator, replacing the DC link capacitor. Simulation results show the proposed ABC-NFC method performs better than ANFIS, ANN, FLC and NFC in compensating for voltage sags.
Voltage stability Analysis using GridCalAnmol Dwivedi
Power system voltage stability is characterized as being capable of maintaining load voltage magnitudes within specified operating limits under steady state conditions. This presentation deals with the modeling of two standard power systems test cases i.e the Nordic-32 and the Nordic-68, comparing the power flows results obtained from GridCal against PSS/E, finding the respective P-V curves for the two test cases using the continuation power flow under contingencies, and finally proposing a graph-based test statistic which can be used for an imminent voltage instability. The simulations are carried out using an open-source power system software called GridCal and the scripts for this project are written in python.
A REVIEW OF SELF HEALING SMART GRIDS USING THE MULTIAGENT SYSTEMijiert bestjournal
This document reviews techniques for self-healing smart grids using multi-agent systems. It summarizes three papers that propose different multi-agent based approaches: 1) A distribution automation solution using substation, load, and restoration agents; 2) A cooperative agent architecture with bus, distributed generator, zone, and global agents; 3) An overload relief strategy using wide area measurements and a unified power flow controller. The techniques aim to automate fault detection, location, and restoration to improve grid reliability through self-healing capabilities.
Swakshar Ray has over 15 years of experience in electrical engineering. He currently works as a scientist at ABB focusing on HVDC and FACTS systems. Previously he held research positions at GE and ABB researching topics like wide area control, power system modeling, and energy storage. He holds a PhD in electrical engineering from the University of Missouri with a focus on intelligent wide area control.
Performance Analysis Of PV Interfaced Neural Network Based Hybrid Active Powe...IJERA Editor
This paper presents a comparative analysis of neural network controlled PV interfaced hybrid active power filter designed for harmonic compensation for nonlinear load.The neural network has been chosen for reference current generation because of its fast adaptiveness, simple calculation and high accuracy to eliminate harmonics.This paper shows a novel approach to interface PV array to hybrid active power filter to keep the capacitor voltage stable. To obtain efficient output from PV Array Maximum power point tracking (MPPT) is employed in it. MPPT is able to extract maximum possible power from PV Array of change in atmospheric condition. Simulation and analysis of hybrid active power filter and PV Array is done under nonlinear load, sudden change in load and unbalanced load conditions. The detailed simulation results have been presented to validate the proposed methodology.
This document describes a study on using artificial neural networks for intelligent real-time power quality monitoring. It provides an overview of the need for real-time monitoring, introduces artificial neural networks and discusses power quality issues. It then describes the proposed neural network model for predicting power demand values and classifying harmonics and abnormal waveforms with over 99% accuracy. The conclusions state that neural networks can make monitoring systems more intelligent and robust by handling noisy data during worst conditions.
In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient training methods called hybrid learning method.The method requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.
Neural wavelet based hybrid model for short-term load forecastingAlexander Decker
This document summarizes a research paper that proposes a neural-wavelet based hybrid model for short-term load forecasting. The paper introduces neural networks and how they can be used for electric load forecasting. It then proposes a model that uses wavelet transforms for preprocessing the original load signal data into different levels, before inputting these into a neural network for short-term load forecasting. The model is tested and results show the neural-wavelet model provides more accurate forecasts than an artificial neural network alone.
The document proposes an artificial bee colony (ABC) algorithm based neuro fuzzy controller (NFC) to improve the performance of a unified power quality conditioner (UPQC) in compensating for power quality issues like voltage sags. The NFC uses the error and change in error voltage as inputs to a neural network. The ABC algorithm is used to optimize the neural network output. This optimized output is then used to generate optimal fuzzy rules and calculate the discharging capacitor voltage from a bias voltage generator, replacing the DC link capacitor. Simulation results show the proposed ABC-NFC method performs better than ANFIS, ANN, FLC and NFC in compensating for voltage sags.
Voltage stability Analysis using GridCalAnmol Dwivedi
Power system voltage stability is characterized as being capable of maintaining load voltage magnitudes within specified operating limits under steady state conditions. This presentation deals with the modeling of two standard power systems test cases i.e the Nordic-32 and the Nordic-68, comparing the power flows results obtained from GridCal against PSS/E, finding the respective P-V curves for the two test cases using the continuation power flow under contingencies, and finally proposing a graph-based test statistic which can be used for an imminent voltage instability. The simulations are carried out using an open-source power system software called GridCal and the scripts for this project are written in python.
A REVIEW OF SELF HEALING SMART GRIDS USING THE MULTIAGENT SYSTEMijiert bestjournal
This document reviews techniques for self-healing smart grids using multi-agent systems. It summarizes three papers that propose different multi-agent based approaches: 1) A distribution automation solution using substation, load, and restoration agents; 2) A cooperative agent architecture with bus, distributed generator, zone, and global agents; 3) An overload relief strategy using wide area measurements and a unified power flow controller. The techniques aim to automate fault detection, location, and restoration to improve grid reliability through self-healing capabilities.
Swakshar Ray has over 15 years of experience in electrical engineering. He currently works as a scientist at ABB focusing on HVDC and FACTS systems. Previously he held research positions at GE and ABB researching topics like wide area control, power system modeling, and energy storage. He holds a PhD in electrical engineering from the University of Missouri with a focus on intelligent wide area control.
Performance Analysis Of PV Interfaced Neural Network Based Hybrid Active Powe...IJERA Editor
This paper presents a comparative analysis of neural network controlled PV interfaced hybrid active power filter designed for harmonic compensation for nonlinear load.The neural network has been chosen for reference current generation because of its fast adaptiveness, simple calculation and high accuracy to eliminate harmonics.This paper shows a novel approach to interface PV array to hybrid active power filter to keep the capacitor voltage stable. To obtain efficient output from PV Array Maximum power point tracking (MPPT) is employed in it. MPPT is able to extract maximum possible power from PV Array of change in atmospheric condition. Simulation and analysis of hybrid active power filter and PV Array is done under nonlinear load, sudden change in load and unbalanced load conditions. The detailed simulation results have been presented to validate the proposed methodology.
This document describes a study on using artificial neural networks for intelligent real-time power quality monitoring. It provides an overview of the need for real-time monitoring, introduces artificial neural networks and discusses power quality issues. It then describes the proposed neural network model for predicting power demand values and classifying harmonics and abnormal waveforms with over 99% accuracy. The conclusions state that neural networks can make monitoring systems more intelligent and robust by handling noisy data during worst conditions.
International Journal of Engineering (IJE) Volume (3) Issue (1)CSCJournals
This document discusses the implementation of artificial intelligence techniques for steady state security assessment in deregulated power system markets. It proposes using neural networks, decision trees, and adaptive neuro-fuzzy inference systems to analyze power transactions between generators and customers in deregulated systems. Data from load flow analysis is used to train and test the AI models. The techniques are tested on various standard power system test cases. The results show that neural networks provide more accurate and faster assessments compared to decision trees and neuro-fuzzy systems, but the latter two may be easier to implement for practical applications. The new methods could help improve security in planning and operating deregulated power system markets.
Cluster Computing Environment for On - line Static Security Assessment of lar...IDES Editor
The increased size of modern power systems
demand faster and accurate means for the security assessment,
so that the decisions for reliable and secure operation planning
could be drawn in a systematic manner. Large computational
overhead is the major impediment in preventing the power
system security assessment (PSSA) from on-line use. To
mitigate this problem, this paper proposes, a cluster computing
based architecture for power system static security assessment,
utilizing the tools in the open source domain. A variant of the
master/slave pattern is used for deploying the cluster of
workstations (COW), which act as the computational engine
for the on-line PSSA. The security assessment is performed
utilizing the developed composite security index that can
accurately differentiate the secure and non-secure cases and
has been defined as a function of bus voltage and line flow
limit violations. Due to the inherent parallel structure of
security assessment algorithm and to exploit the potential of
distributed computing, domain decomposition is employed for
parallelizing the sequential algorithm. Extensive
experimentations were carried out on IEEE 57 bus and IEEE
145-bus 50 machine standard test systems for demonstrating
the validity of the proposed architecture.
On-line Power System Static Security Assessment in a Distributed Computing Fr...idescitation
The computation overhead is of major concern when
going for increased accuracy in online power system security
assessment (OPSSA). This paper proposes a scalable solution
technique based on distributed computing architecture to
mitigate the problem. A variant of the master/slave pattern is
used for deploying the cluster of workstations (COW), which
act as the computational engine for the OPSSA. Owing to the
inherent parallel structure in security analysis algorithm, to
exploit the potential of distributed computing, domain
decomposition is adopted instead of functional decomposition.
The security assessment is performed utilizing the developed
composite security index that can accurately differentiate the
secure and non-secure cases and has been defined as a function
of bus voltage and line flow limit violations. Validity of
proposed architecture is demonstrated by the results obtained
from an intensive experimentation using the benchmark IEEE
57 bus test system. The proposed framework, which is scalable,
can be further extended to intelligent monitoring and control
of power system
Reliability Prediction of Port Harcourt Electricity Distribution Network Usin...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Power System Problems in Teaching Control Theory on Simulinkijctcm
This experiment demonstrates to engineering students that control system and power system theory are not orthogonal, but highly interrelated. It introduces a real-world power system problem to enhance time domain State Space Modelling (SSM) skills of students. It also shows how power quality is affected with real-world scenarios. Power system was modeled in State Space by following its circuit topology in a bottom-up fashion. At two different time instances of the power generator sinusoidal wave, the transmission line was switched on. Fourier transform was used to analyze resulting line currents. It validated the harmonic components, as expected, from power system theory. Students understood the effects of switching transients at various times on supply voltage sinusoid within control theory and learned time domain analysis. They were surveyed to gauge their perception of the project. Results from a before/after assessment analyzed usingT-Tests showed a statistically significant enhanced learning in SSM.
Probabilistic Performance Index based Contingency Screening for Composite Pow...IJECEIAES
Composite power system reliability involves assessing the adequacy of generation and transmission system to meet the demand at major system load points. Contingency selection was being the most tedious step in reliability evaluation of large electric systems. Contingency in power system might be a possible event in future which was not predicted with certainty in earlier research. Therefore, uncertainty may be inevitable in power system operation. Deterministic indices may not guarantee the randomness in reliability assessment. In order to account for volatility in contingencies, a new performance index proposed in the current research. Proposed method assimilates the uncertainty in computational procedure. Reliability test systems like Roy Billinton Test System-6 bus system and IEEE-24 bus reliability test systems were used to test the effectiveness of a proposed method.
INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MININ...ijscai
In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Modern Tools for the Small-Signal Stability Analysis and Design of FACTS Assi...Power System Operation
There has recently been an intense development of eigenanalysis tools for power systems dynamics and control. This paper provides an up-to-date review of the major algorithms available. Comments on other relevant but yet unpublished algorithms are also made.
The conventional state-space description is obtained through the elimination of the algebraic variables of the mathematical model. In many applications, the power system case included, the algebraic variables in the model are usually more numerous than the state variables.
The problem of multiple, decentralized controller synthesis in large, FACTS assisted power systems is addressed.
A report in CIGRE [1] recognized the large benefits of working with the unreduced set of differential-algebraic equations linearized at an operating point. The resulting power system Jacobian is large and highly sparse, allowing efficient use of sparsity oriented programming. The computation of all system eigenvalues, not practical for large power systems, was since then replaced by the computation of only the eigenvalues of interest.
A good part of the paper is dedicated to showing the high productivity gains achieved when using a well designed package for graphical display and animation of results.
The pressing needs for better utilization of existing transmission and generation equipment will eventually dictate a wider use of FACTS devices. The higher complexity and stricter requirements on power system controls, under both steady-state and dynamic conditions, calls for the immediate development and use of more sophisticated computer tools. The material in this paper may help the further development of one of these tools.
Many important developments occurred in the last fifteen
IRJET- A Review on SVM based Induction MotorIRJET Journal
This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
1. It discusses using an artificial immune system-optimized SVM for detecting broken rotor bars and stator faults in induction motors based on motor current data.
2. It describes using wavelet analysis, principal component analysis, and SVM classification to detect faults like frequency variations, unbalanced voltages, and interturn shorts based on motor current spectra.
3. It proposes using dq0 voltage components analyzed with fast Fourier transforms as features for an SVM classifier to detect stator winding shorts, achieving over 98% accuracy.
Efficient decentralized iterative learning tracker for unknown sampled data i...ISA Interchange
In this paper, an efficient decentralized iterative learning tracker is proposed to improve the dynamic performance of the unknown controllable and observable sampled-data interconnected large-scale state-delay system, which consists of NN multi-input multi-output (MIMO) subsystems, with the closed-loop decoupling property. The off-line observer/Kalman filter identification (OKID) method is used to obtain the decentralized linear models for subsystems in the interconnected large-scale system. In order to get over the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is developed to replace the observer identified by OKID. Then, the iterative learning control (ILC) scheme is integrated with the high-gain tracker design for the decentralized models. To significantly reduce the iterative learning epochs, a digital-redesign linear quadratic digital tracker with the high-gain property is proposed as the initial control input of ILC. The high-gain property controllers can suppress uncertain errors such as modeling errors, nonlinear perturbations, and external disturbances (Guo et al., 2000) [18]. Thus, the system output can quickly and accurately track the desired reference in one short time interval after all drastically-changing points of the specified reference input with the closed-loop decoupling property.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Artificial Neural Network Based Load ForecastingIRJET Journal
This document discusses using artificial neural networks for short-term load forecasting. It compares the performance of two training algorithms - Multiple Layer Perceptron (MLP) and Least Mean Square (LMS). When MLP was used, the mean absolute percent error was 11.424%. When LMS was used, the error rate decreased and the mean absolute percent error improved to 2.64%, showing more accurate forecasting results. In conclusion, artificial neural networks are useful for short-term load forecasting and the LMS algorithm produced more promising results compared to MLP.
DYNAMIC VOLTAGE SCALING FOR POWER CONSUMPTION REDUCTION IN REAL-TIME MIXED TA...cscpconf
The reduction in energy consumption without any deadline miss is one of the main challenges in real-time embedded systems. Dynamic voltage scaling (DVS) is a technique that reduces the power consumption of processors by utilizing various operating points provided to the DVS processor. These operating points consist of pairs of voltage and frequency. The selection of operating points can be done based on the load to the system at a particular point of time. In this work DVS is applied to both periodic and sporadic tasks, and an average of 40% of energy is reduced. The energy consumption of the processor is further reduced by 2-10% by reducing the number of pre-emption and frequency switching
This paper based on review of load flow analysis of radial distribution system. The
problem on unbalancing of reactive power is in single phase and three phases. Therefore to improve &
enhancing voltage profile and stability of the existing power system, load flow analysis is alternative
solution. Here is review on different approaches by different author’s for load flow analysis in three phase
radial distribution system to improve voltage stability and to minimize the transmission line losses.
Different optimization techniques may be use to identify as well as applied in three phase radial
distribution system with analysis of different authors review and based on merits and demerits of radial
distribution system. Local search optimization is also described based on this review.
Power Flow Analysis of Island Business District 33KV Distribution Grid System...IJERA Editor
The solution to power flow is one of the most important problems in electrical power systems. Traditional methods have been previously used for power flow analysis, but with prevalent drawbacks such as abnormal operating solutions and divergences in heavy loads. This paper presents power flow analysis in a power system, by modelling a typical 33kV Distribution Network, and simulating using the NEPLAN software for power flow studies. Island Business Unit’s (IBU) 33kV network of Eko Electricity Distribution Plc (EKEDP) for a scenario day is taken as case study in the analysis. The most important parameters of power flow analysis is utilized to find the magnitude and phase angles of the voltages at each Busbar, as well as the real and reactive power flowing through each distribution line within the network under consideration.
This document summarizes a study that performed contingency analysis on Nigeria's 330kV power transmission network to identify vulnerabilities. Fast Decoupled Load Flow (FDLF) analysis was used to simulate the impact of single line outages. When the Kainji-GS line went down, several buses experienced low voltages and several lines saw power losses over 5%. The performance indices of each line were calculated and ranked based on their severity to identify the most critical lines.
Contingency plans based on N - 1 and N - 2 contingencies are already very much used by utilities . Artificial intelligent methods are new trends for analysing the contingency scenario along with state of art congestion management. This gives extra backup and b oost to reliable operation under contingent scenario of power system. This paper envisages the summary of all those efforts. This paper will help utilities to put more thinking in terms of recent developments in fast and intelligent computing methods. The paper highlights classical research and modern trends in contingency analysis such as hybrid artificial intelligent methods. Steady state stability assessment of a power system pursues a twofold objective:first to appraise the system's capability to withs tand major contingencies,and second to suggest remedial actions,i.e. means to enhance this capability,whenever needed. The first objective is the concern of analysis,the second is a matter of control.
Predicting Post Outage Transmission Line Flows using Linear Distribution FactorsDr. Amarjeet Singh
In order to design and implement preventive
and remedial actions, a continuous performance of fast
security analysis is imperative amid outages of system
components. Following the contingency of a system
component, State estimation and Load flow techniques
are the two popular techniques used to determine
system state variables leading to estimation of flows,
losses and violations in nodal voltages and transmission
line flows. But the dynamic state and complexity of the
system requires faster means of estimations which can
be achieved by linear distribution factors. The use of
Distribution factors in form of Power Transfer
Distribution Factors (PTDF) and Line Outage
Distribution Factors (LODF) which are transmission
line sensitivities with respect to active power exchanges
between buses and transmission line outages offer an
alternative to these two techniques being linear,
quicker, and non-iterative. Following the estimation of
the linear distribution factors from a reference
operating point (base case) and contingency cases
involving line outage, generator output variation and
outage of a Six bus network using Matlab programs,
the results show that by means of Linear Distribution
factors quick estimates of post outage line flows can be
made which match flow results obtained from DC load
flow analysis.
IRJET- Voltage Stability, Loadability and Contingency Analysis with Optimal I...IRJET Journal
This document discusses contingency analysis and optimal placement of renewable distributed generators (RDGs) using continuation power flow analysis to improve voltage stability and loadability. It presents a methodology to determine the optimal location and mix of different RDG technologies (solar, wind, fuel cells) on the IEEE 9-bus test system using the Power System Analysis Toolbox (PSAT). Reactive power performance indices are calculated for different line contingencies to identify critical buses. The results show that optimally placing RDGs can enhance voltage stability and increase the maximum loadability point compared to the base case without RDGs.
Power System Contingency Ranking Using Fast Decoupled Load Flow Methodpaperpublications3
Abstract: Voltage instability is the phenomena associated with heavily loaded power systems. It is normally aggravated due to large disturbance. The Power system security is one of the significant aspects, where the proper action needs to be taken for the unseen contingency. In the event of contingency, the most serious threat to operation and control of power system is insecurity. Therefore, the contingency analysis is a key for the power system security. The contingency ranking using the performance index is a method for the line outages in a power system, which ranks the highest performance index line first and proceeds in a descending manner based on the calculated PI for all the line outages. This helps to take the prior action to keep the system secure. In this paper Fast Decoupled power flow method is used for the power system contingency ranking for the line outage based on the Active power and Voltage performance index. The ranking is given by considering the overall performance index, which is the summation of Active power and voltage performance index. The proposed method is implemented on a IEEE-14 bus system.
International Journal of Engineering (IJE) Volume (3) Issue (1)CSCJournals
This document discusses the implementation of artificial intelligence techniques for steady state security assessment in deregulated power system markets. It proposes using neural networks, decision trees, and adaptive neuro-fuzzy inference systems to analyze power transactions between generators and customers in deregulated systems. Data from load flow analysis is used to train and test the AI models. The techniques are tested on various standard power system test cases. The results show that neural networks provide more accurate and faster assessments compared to decision trees and neuro-fuzzy systems, but the latter two may be easier to implement for practical applications. The new methods could help improve security in planning and operating deregulated power system markets.
Cluster Computing Environment for On - line Static Security Assessment of lar...IDES Editor
The increased size of modern power systems
demand faster and accurate means for the security assessment,
so that the decisions for reliable and secure operation planning
could be drawn in a systematic manner. Large computational
overhead is the major impediment in preventing the power
system security assessment (PSSA) from on-line use. To
mitigate this problem, this paper proposes, a cluster computing
based architecture for power system static security assessment,
utilizing the tools in the open source domain. A variant of the
master/slave pattern is used for deploying the cluster of
workstations (COW), which act as the computational engine
for the on-line PSSA. The security assessment is performed
utilizing the developed composite security index that can
accurately differentiate the secure and non-secure cases and
has been defined as a function of bus voltage and line flow
limit violations. Due to the inherent parallel structure of
security assessment algorithm and to exploit the potential of
distributed computing, domain decomposition is employed for
parallelizing the sequential algorithm. Extensive
experimentations were carried out on IEEE 57 bus and IEEE
145-bus 50 machine standard test systems for demonstrating
the validity of the proposed architecture.
On-line Power System Static Security Assessment in a Distributed Computing Fr...idescitation
The computation overhead is of major concern when
going for increased accuracy in online power system security
assessment (OPSSA). This paper proposes a scalable solution
technique based on distributed computing architecture to
mitigate the problem. A variant of the master/slave pattern is
used for deploying the cluster of workstations (COW), which
act as the computational engine for the OPSSA. Owing to the
inherent parallel structure in security analysis algorithm, to
exploit the potential of distributed computing, domain
decomposition is adopted instead of functional decomposition.
The security assessment is performed utilizing the developed
composite security index that can accurately differentiate the
secure and non-secure cases and has been defined as a function
of bus voltage and line flow limit violations. Validity of
proposed architecture is demonstrated by the results obtained
from an intensive experimentation using the benchmark IEEE
57 bus test system. The proposed framework, which is scalable,
can be further extended to intelligent monitoring and control
of power system
Reliability Prediction of Port Harcourt Electricity Distribution Network Usin...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Power System Problems in Teaching Control Theory on Simulinkijctcm
This experiment demonstrates to engineering students that control system and power system theory are not orthogonal, but highly interrelated. It introduces a real-world power system problem to enhance time domain State Space Modelling (SSM) skills of students. It also shows how power quality is affected with real-world scenarios. Power system was modeled in State Space by following its circuit topology in a bottom-up fashion. At two different time instances of the power generator sinusoidal wave, the transmission line was switched on. Fourier transform was used to analyze resulting line currents. It validated the harmonic components, as expected, from power system theory. Students understood the effects of switching transients at various times on supply voltage sinusoid within control theory and learned time domain analysis. They were surveyed to gauge their perception of the project. Results from a before/after assessment analyzed usingT-Tests showed a statistically significant enhanced learning in SSM.
Probabilistic Performance Index based Contingency Screening for Composite Pow...IJECEIAES
Composite power system reliability involves assessing the adequacy of generation and transmission system to meet the demand at major system load points. Contingency selection was being the most tedious step in reliability evaluation of large electric systems. Contingency in power system might be a possible event in future which was not predicted with certainty in earlier research. Therefore, uncertainty may be inevitable in power system operation. Deterministic indices may not guarantee the randomness in reliability assessment. In order to account for volatility in contingencies, a new performance index proposed in the current research. Proposed method assimilates the uncertainty in computational procedure. Reliability test systems like Roy Billinton Test System-6 bus system and IEEE-24 bus reliability test systems were used to test the effectiveness of a proposed method.
INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MININ...ijscai
In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Modern Tools for the Small-Signal Stability Analysis and Design of FACTS Assi...Power System Operation
There has recently been an intense development of eigenanalysis tools for power systems dynamics and control. This paper provides an up-to-date review of the major algorithms available. Comments on other relevant but yet unpublished algorithms are also made.
The conventional state-space description is obtained through the elimination of the algebraic variables of the mathematical model. In many applications, the power system case included, the algebraic variables in the model are usually more numerous than the state variables.
The problem of multiple, decentralized controller synthesis in large, FACTS assisted power systems is addressed.
A report in CIGRE [1] recognized the large benefits of working with the unreduced set of differential-algebraic equations linearized at an operating point. The resulting power system Jacobian is large and highly sparse, allowing efficient use of sparsity oriented programming. The computation of all system eigenvalues, not practical for large power systems, was since then replaced by the computation of only the eigenvalues of interest.
A good part of the paper is dedicated to showing the high productivity gains achieved when using a well designed package for graphical display and animation of results.
The pressing needs for better utilization of existing transmission and generation equipment will eventually dictate a wider use of FACTS devices. The higher complexity and stricter requirements on power system controls, under both steady-state and dynamic conditions, calls for the immediate development and use of more sophisticated computer tools. The material in this paper may help the further development of one of these tools.
Many important developments occurred in the last fifteen
IRJET- A Review on SVM based Induction MotorIRJET Journal
This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
1. It discusses using an artificial immune system-optimized SVM for detecting broken rotor bars and stator faults in induction motors based on motor current data.
2. It describes using wavelet analysis, principal component analysis, and SVM classification to detect faults like frequency variations, unbalanced voltages, and interturn shorts based on motor current spectra.
3. It proposes using dq0 voltage components analyzed with fast Fourier transforms as features for an SVM classifier to detect stator winding shorts, achieving over 98% accuracy.
Efficient decentralized iterative learning tracker for unknown sampled data i...ISA Interchange
In this paper, an efficient decentralized iterative learning tracker is proposed to improve the dynamic performance of the unknown controllable and observable sampled-data interconnected large-scale state-delay system, which consists of NN multi-input multi-output (MIMO) subsystems, with the closed-loop decoupling property. The off-line observer/Kalman filter identification (OKID) method is used to obtain the decentralized linear models for subsystems in the interconnected large-scale system. In order to get over the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is developed to replace the observer identified by OKID. Then, the iterative learning control (ILC) scheme is integrated with the high-gain tracker design for the decentralized models. To significantly reduce the iterative learning epochs, a digital-redesign linear quadratic digital tracker with the high-gain property is proposed as the initial control input of ILC. The high-gain property controllers can suppress uncertain errors such as modeling errors, nonlinear perturbations, and external disturbances (Guo et al., 2000) [18]. Thus, the system output can quickly and accurately track the desired reference in one short time interval after all drastically-changing points of the specified reference input with the closed-loop decoupling property.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Artificial Neural Network Based Load ForecastingIRJET Journal
This document discusses using artificial neural networks for short-term load forecasting. It compares the performance of two training algorithms - Multiple Layer Perceptron (MLP) and Least Mean Square (LMS). When MLP was used, the mean absolute percent error was 11.424%. When LMS was used, the error rate decreased and the mean absolute percent error improved to 2.64%, showing more accurate forecasting results. In conclusion, artificial neural networks are useful for short-term load forecasting and the LMS algorithm produced more promising results compared to MLP.
DYNAMIC VOLTAGE SCALING FOR POWER CONSUMPTION REDUCTION IN REAL-TIME MIXED TA...cscpconf
The reduction in energy consumption without any deadline miss is one of the main challenges in real-time embedded systems. Dynamic voltage scaling (DVS) is a technique that reduces the power consumption of processors by utilizing various operating points provided to the DVS processor. These operating points consist of pairs of voltage and frequency. The selection of operating points can be done based on the load to the system at a particular point of time. In this work DVS is applied to both periodic and sporadic tasks, and an average of 40% of energy is reduced. The energy consumption of the processor is further reduced by 2-10% by reducing the number of pre-emption and frequency switching
This paper based on review of load flow analysis of radial distribution system. The
problem on unbalancing of reactive power is in single phase and three phases. Therefore to improve &
enhancing voltage profile and stability of the existing power system, load flow analysis is alternative
solution. Here is review on different approaches by different author’s for load flow analysis in three phase
radial distribution system to improve voltage stability and to minimize the transmission line losses.
Different optimization techniques may be use to identify as well as applied in three phase radial
distribution system with analysis of different authors review and based on merits and demerits of radial
distribution system. Local search optimization is also described based on this review.
Power Flow Analysis of Island Business District 33KV Distribution Grid System...IJERA Editor
The solution to power flow is one of the most important problems in electrical power systems. Traditional methods have been previously used for power flow analysis, but with prevalent drawbacks such as abnormal operating solutions and divergences in heavy loads. This paper presents power flow analysis in a power system, by modelling a typical 33kV Distribution Network, and simulating using the NEPLAN software for power flow studies. Island Business Unit’s (IBU) 33kV network of Eko Electricity Distribution Plc (EKEDP) for a scenario day is taken as case study in the analysis. The most important parameters of power flow analysis is utilized to find the magnitude and phase angles of the voltages at each Busbar, as well as the real and reactive power flowing through each distribution line within the network under consideration.
This document summarizes a study that performed contingency analysis on Nigeria's 330kV power transmission network to identify vulnerabilities. Fast Decoupled Load Flow (FDLF) analysis was used to simulate the impact of single line outages. When the Kainji-GS line went down, several buses experienced low voltages and several lines saw power losses over 5%. The performance indices of each line were calculated and ranked based on their severity to identify the most critical lines.
Contingency plans based on N - 1 and N - 2 contingencies are already very much used by utilities . Artificial intelligent methods are new trends for analysing the contingency scenario along with state of art congestion management. This gives extra backup and b oost to reliable operation under contingent scenario of power system. This paper envisages the summary of all those efforts. This paper will help utilities to put more thinking in terms of recent developments in fast and intelligent computing methods. The paper highlights classical research and modern trends in contingency analysis such as hybrid artificial intelligent methods. Steady state stability assessment of a power system pursues a twofold objective:first to appraise the system's capability to withs tand major contingencies,and second to suggest remedial actions,i.e. means to enhance this capability,whenever needed. The first objective is the concern of analysis,the second is a matter of control.
Predicting Post Outage Transmission Line Flows using Linear Distribution FactorsDr. Amarjeet Singh
In order to design and implement preventive
and remedial actions, a continuous performance of fast
security analysis is imperative amid outages of system
components. Following the contingency of a system
component, State estimation and Load flow techniques
are the two popular techniques used to determine
system state variables leading to estimation of flows,
losses and violations in nodal voltages and transmission
line flows. But the dynamic state and complexity of the
system requires faster means of estimations which can
be achieved by linear distribution factors. The use of
Distribution factors in form of Power Transfer
Distribution Factors (PTDF) and Line Outage
Distribution Factors (LODF) which are transmission
line sensitivities with respect to active power exchanges
between buses and transmission line outages offer an
alternative to these two techniques being linear,
quicker, and non-iterative. Following the estimation of
the linear distribution factors from a reference
operating point (base case) and contingency cases
involving line outage, generator output variation and
outage of a Six bus network using Matlab programs,
the results show that by means of Linear Distribution
factors quick estimates of post outage line flows can be
made which match flow results obtained from DC load
flow analysis.
IRJET- Voltage Stability, Loadability and Contingency Analysis with Optimal I...IRJET Journal
This document discusses contingency analysis and optimal placement of renewable distributed generators (RDGs) using continuation power flow analysis to improve voltage stability and loadability. It presents a methodology to determine the optimal location and mix of different RDG technologies (solar, wind, fuel cells) on the IEEE 9-bus test system using the Power System Analysis Toolbox (PSAT). Reactive power performance indices are calculated for different line contingencies to identify critical buses. The results show that optimally placing RDGs can enhance voltage stability and increase the maximum loadability point compared to the base case without RDGs.
Power System Contingency Ranking Using Fast Decoupled Load Flow Methodpaperpublications3
Abstract: Voltage instability is the phenomena associated with heavily loaded power systems. It is normally aggravated due to large disturbance. The Power system security is one of the significant aspects, where the proper action needs to be taken for the unseen contingency. In the event of contingency, the most serious threat to operation and control of power system is insecurity. Therefore, the contingency analysis is a key for the power system security. The contingency ranking using the performance index is a method for the line outages in a power system, which ranks the highest performance index line first and proceeds in a descending manner based on the calculated PI for all the line outages. This helps to take the prior action to keep the system secure. In this paper Fast Decoupled power flow method is used for the power system contingency ranking for the line outage based on the Active power and Voltage performance index. The ranking is given by considering the overall performance index, which is the summation of Active power and voltage performance index. The proposed method is implemented on a IEEE-14 bus system.
IRJET- A Simple Approach to Identify Power System Transmission Line Faults us...IRJET Journal
This paper presents a Probabilistic Neural Network (PNN) approach for identifying and classifying faults on power transmission lines. The PNN is trained on voltage waveform data simulated using Electromagnetic Transient Program (EMTP) software for different fault types and locations on a 150km transmission line. Only two sets of simulated data are used to train the PNN, requiring less computation than other methods that preprocess data. The trained PNN is able to accurately identify and classify fault types based on the voltage waveform, which helps ensure reliable power transmission by isolating only faulty lines or phases.
IRJET- Feed-Forward Neural Network Based Transient Stability Assessment o...IRJET Journal
This document discusses using a feed-forward neural network to assess transient stability and determine critical clearing times in a 132kV power system network in Nigeria. It first models the Afam to Port Harcourt network in ETAP software to simulate faults. MATLAB is then used to solve swing equations from the simulations. Selected data is fed into a feed-forward neural network to map out critical clearing times required by circuit breakers. The results show neural networks can complement conventional methods to evaluate critical clearing times and rotor angles for transient stability assessment of power networks.
Influencing Factors on Power Losses in Electric Distribution NetworkIJAEMSJORNAL
Line losses reduction greatly affects the performance of the electric distribution network. This paper aims to identify the influencing factors causing power losses in that network. Newton-Raphson method is used for the loss assessment and the Sensitivity analysis by approach One-Factor-At-A-Time (OAT) for the influencing factors identification. Simulation with the meshed IEEE-30 bus test system is carried out under MATLAB environment. Among the 14 parameters investigated of each line, the result shows that the consumed reactive powers by loads, the bus voltages and the linear parameters are the most influencing on the power losses in several lines. Thus, in order to optimize these losses, the solution consists of the reactive power compensation by using capacitor banks; then the placement of appropriate components in the network according to the corresponding loads; and finally, the injection of other energy sources into the bus which recorded high level losses by using the hybrid system for instance.
Power electronics play a significant role in different areas of technology, more usage of power electronic devices lead to more harmonic content and various power quality issues in the system. Therefore, power quality gains more significance in the current era of research. Power electronics equipment’s with non-linear loads concludes with more harmonic disturbances and lower power factor. Harmonic impurities are the major problem ingredient due to the connection of non-linear load. To lessen the harmonics usually passive filters are used. The major objective of this work is to monitor and analyse the power quality of uninterrupted power supply by means of DAQ system that gathers real time data on the system and then the data is analysed using National Instruments LabVIEW. Once power quality analysis is done, a new technique of filter implementation using output transformer of the UPS was explored and passive filter was simulated using MATLAB/Simulink and then simulated filter was implemented in order to achieve power quality improvement.
Impact of Electric Vehicle Integration on Gridvivatechijri
Load flow analysis is most essential and important approach to investigate problems in power system. It can provide balance steady state operation of power system without considering transients in it. This project presents a new and efficient method for solving the Load flow problem of a distribution network. By using Backward/Forward sweep method parameters like voltage profile, total power losses, load on each bus of the Distribution Network will be known. By using Load Flow load balancing of the Distribution system can be achieved. For load balancing we will use the power stored in the Electric vehicle. As Electric vehicle has large battery pack for storage. The impact of Electric Vehicle and load flow of distribution network is computer programed to implement the power flow solution scheme in MATLAB software.
(1) This document discusses implementing discrete wavelet transformation (DWT) using VHDL for power system analysis. DWT provides time-frequency localization and can detect minor faults in power transformers. VHDL is used to design a digital DWT architecture on FPGA for improved efficiency over traditional methods. (2) The architecture performs multi-level DWT decomposition using high pass and low pass filters to extract detail and approximation coefficients from input signals. (3) This allows more accurate fault detection compared to conventional spatial analysis and can provide faster response for electrical equipment monitoring than existing DSP or filter chip methods.
IRJET - Fault Detection and Classification in Transmission Line by using KNN ...IRJET Journal
This document presents a machine learning approach using K-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers to detect and classify faults on a transmission line. Discrete Wavelet Transform is used to extract features from fault current and voltage signals. These features are input to the KNN and DT classifiers, which are compared to determine the most suitable technique for fault analysis. KNN classifies based on closest data points while DT recursively splits data based on attribute choices until classification is reached. The proposed approach uses semi-supervised learning to process both labeled and unlabeled power system data for fault detection and classification.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Short Term Load Forecasting: One Week (With & Without Weekend) Using Artifici...IJLT EMAS
This paper present for analysis of short term load forecasting: one week (with & without weekend) using ANN techniques for SLDC of Gujarat. In this paper short term electric load forecasting using neural network; based on historical load demand, The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB.12 ANN tool box. Design a model for one week (with & w/o weekend) load pattern for STLF using the neural network have been input variables are (Min., Avg., & Max. load demands for previous week, Min., Avg., & Max. temperature for previous week & Min., Avg., & Max. humidity for previous week). And Nov-12 to Apr-13 (6 Months) historical load data from the SLDC, Gujarat are used for training, testing and showing the good performance. Using this ANN model computing the mean absolute error between the exact and predicted values, we were able to obtain an absolute mean error within specified limit and regression value close to one. This represents a high degree of accuracy.
Shashwat Shekhar's work portfolio summarizes his education and experience in electrical engineering. He has a Master's degree from the University of Texas at Arlington in electrical engineering and interned at Xcel Energy doing transmission planning engineering work, including NERC reliability standard contingency analyses. His internship responsibilities involved power flow and transient stability modeling, contingency testing, and developing VBA code. He also interned in India performing generator interconnection analysis. His academic projects include designing an inverse-time overcurrent relay and developing algorithms for static VAR compensation and adaptive solar energy prediction.
A hybrid approach of artificial neural network-particle swarm optimization a...IJECEIAES
This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.
ENHANCEMENT OF POWER SYSTEM SECURITY USING PSO-NR OPTIMIZATION TECHNIQUEIAEME Publication
Maintaining power system security is one of the challenging tasks for the power system engineers. The security assessment is an essential task as it gives the knowledge about the system state in the event of a contingency. Contingency analysis technique is being widely used to predict the effect of outages like failures of equipment, transmission line etc., and to take necessary actions to keep the power system secure and reliable. The off line analysis to predict the effect of individual contingency is a tedious task as a power system contains large number of components. Practically, only selected contingencies will lead to severe conditions in power system.
A Novel Study on Bipolar High Voltage Direct Current Transmission Lines Prote...IJECEIAES
In long dc transmission lines identification of fault is important for transferring a large amount of power. In bipolar Line commutated converter transmission lines are subjected to harsh weather condition so accurate and rapid clearance of fault is essential. A comparative study of the bipolar system with both converters healthy and one converter tripped is studied. Most of the research paper has focussed on transmission line faults in bipolar mode but none of them had focussed when HVDC system works in monopolar mode after the fault. In the proposed scheme the voltage signals are extracted from both poles of the rectifier ends and are processed to identify the faults in transmission lines.The Artificial neural network is utilised in detecting the fault in both bipolar and monopolar system. Since it can identify the relationship between input and output data to detect the fault pattern it can be utilised under all conditions. Moreover, benefits of the proposed method are its accuracy, no requirement of the communication system as it acquires data from one end and has a reach setting of 99%.
Similar to Convergence Problems Of Contingency Analysis In Electrical Power Transmission System (20)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
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4. BCP, Surveying volume 1
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বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Convergence Problems Of Contingency Analysis In Electrical Power Transmission System
1. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 12
Convergence Problems Of Contingency Analysis In Electrical
Power Transmission System
Taofik Adekunle Boladale taboladale@lautech.edu.ng
Works Department,
Ladoke Akintola University of Technology.
Ogbomoso, 210214, Nigeria.
Gafari Abiola Adepoju gaadepoju@lautech.edu.ng
Facuty of Engineering/ Electronic and Electrical Department
Ladoke Akintola University of Technology.
Ogbomoso, 210214, Nigeria
Abstract
Contingency analysis is a tool used by power system engineers for planning and assessing
power system reliability. The conventional analytical method which is mathematical model based,
is not only tedious and time consuming in view of the large number of components in the network
but always left some critical components unassessed due to non-convergence of the power flow
analysis of such, hence the contingency analysis of such system could not be said to be
completed.
In this work, contingency analysis of line components of a standard IEEE-30 Bus and real 330-kV
Nigerian Transmission Company of Nigeria (TCN) network (28Bus) systems were investigated
using Radial Basis Function Neural Network (RBF-NN) which is artificial intelligence based.
The contingency analysis was carried out by solving the non-linear algebraic equations of steady
state model for the standard IEEE-30 Bus and TCN-28 Bus power networks using Newton-
Raphson (N-R) power flow method. RBF-NN method was used for the computation of Reactive
and Active performance indices (PIR and PIA ) which were ranked in order to reveal the criticality
of each line outage. Simulation was carried out using MATLAB R2013a version. The non-
converged lines in both systems were reinforced and re-analysed. The results of contingency
analyses of the reinforced systems show more robust systems with complete line ranking.
Keywords: Contingency Analysis, Analytical method, RBF-NN method, Active and Reactive
Performance Indices, Ranking .
1. INTRODUCTION
The electrical power system network comprises of Generating stations, Circuit Breakers,
Transformers, Transmission lines and the likes, all of which have different operating limits. These
limits determine to a large extent the reliability of the network. The degree however is much
worse in third world countries where poor planning, inadequate financing and lack of good
maintenance culture are the order of the day.
The challenges of ensuring the security of Power system is therefore an enormous task that must
be contended with and the effect of weather (Storms, Earthquake, Tornadoes and so on ), human
factors (sabotage, terrorism, illegal Building constructions just to mention a few ) and bad Political
structures (corruption, nepotism and favouritisms ) especially in developing countries are not
helping the already worsen situations. The major focus of power system Engineers is how to
ensure that the outages are minimised in terms of frequencies of occurrence and durations of
power outages. Since the outages cannot be completely avoided, a contingency arrangement
2. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 13
must be put in place in order to minimise the effect of shutdown resulting from the power outage
(Onohaebi, 2009).
In order to have a reliable network, continuous monitoring of the system where parameters are
continuously checked and adjusted as may be required from time to time through the use of
telemetry system or by a Supervisory Control and Data Acquisition (SCADA) equipment is
therefore required. The continuous checks and balances required in order to have a stable and
reliable system is therefore termed the contingencies control strategies. This however, involves
knowing the operating limits of each integral component of the network prior to and after the
occurrence of the contingency.
Contingency analysis is therefore time consuming and cumbersome since the operating limit of
individual components must be known. The most common approach of Contingency analysis is to
carry-out the power (load) flow analysis and compute performance indices following each
possible outage. However, in view of the cumbersomeness of the power network and the rigorous
calculations and time involve; the use of conventional approach is found to be limited and
ineffective especially on online real life systems (Shahnawaaz and Vijayalaxi, 2014). Amit (2011)
and Naik (2014) have therefore recently researched into the utilisation of the proficiency of
artificial intelligence for contingency analysis of small standard IEEE systems. This research work
therefore considers implementation of the radial basis function neural network (RBF-NN) on
medium size power transmission systems and to establish its application on real life power
system, using 330kV Nigerian transmission system as a case study.
Contingency analysis is conventionally carried out using either contingency selection (ranking) or
screening methods. Contingency ranking is achieved using Performance Indices while screening
is better achieved using fast and approximate network solution (Ejebe et. al., 1998). Contingency
screening (or selection) is therefore an important task which help to minimise the rigorous and
tasking computation, due to the complexity of the network and helps to determine the most
severe contingency as well as the degree of severity.
Generally, the Contingency analysis applies the Kirchorff Current law (Nodal Analysis) to
calculate the current injection into various buses of the power system network (Kusic, 2009);
however the most accurate methods are based on AC Power (load) flow computation which are
solved by iteration techniques (Maghrabi et al, 1998). The AC Power (load) flow are Gauss
Seidel, Newton Raphson (N-R) and Fast decoupled load flow, Gauss Seidel ought to be the most
accurate but its use is limited to small systems due its slow rate of convergence. N-R method on
the other hand has the largest convergence rate but it requires large computer memory while
Fast Decoupled method is the least accurate of the three (Adejumobi et al., 2013). The speedy
convergence advantage of N-R method irrespective of the system size prompted its choice in this
work.
Artificial neural networks in simple feed-forward topology is used in the formulation of Radial
basis function network and for Contingency analysis it is usually combined with Unsupervised
learning. The merits of both according to Boudour and Hellal (2004); are high rate of
convergence even on complex mapping problems, simple structure, fast and efficient training
process and absence of local minimal problem. It also has the capability of accommodating
newer data without any need for retraining. These reasons prompted the choice of Radial basis
function neural network (RBF-NN) for this work and its representation in simplest form is shown in
Figure1.
3. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 14
Y1
X2
X2 Y2 Y2
X3 Y3
Yn
Xn
Input Layer Weighted connections
Hidden Layer
FIGURE 1: Radial basis function (Artificial) neural network (RBF-NN)
General Structure (Andrej et. al., 2011).
2. RESEARCH METHODOLOGY
The contingency analysis was carried out by solving the non-linear algebraic equations of steady
state model for the standard IEEE-30 Bus and TCN-28 Bus power networks using Newton-
Raphson (N-R) power flow method. RBF-NN method was used for the computation of Active and
Reactive performance indices (PIA and PIR ), with the data generated from Analytical method.
Simulation was carried out using MATLAB R2013a version. The non-converged lines in both
systems were noted for reinforcement using additional lines of the same parameters, the
contingency analyses of the reinforced systems were then carried out.
2.1 Analytical Method
The conventional analytical method of carrying out contingency analysis involve; simulation of
contingency (outage), performing power (load) flow and computing the performance indices
following each contingency. Contingency Analysis of the IEEE-30 Bus Standard system and 28
Bus of Transmission Company of Nigeria (TCN) system, using Newton-Raphson (N-R) load flow
analysis and Analytical method reported by Ejebe et al., (1998) which was used to determine the
system performance indices as expressed in equations 1, 2 and 3.
2.1.1 Active Power Performance Index
Active Power performance Index (PIA) is a function of power flow limit violation of line and it is
given by equation 1 as:
r
N
i i
i
A
P
P
r
W
PI
2
1 (max)2
L
1
where
Pi = Active (MW) power flowing on line i prior to the line outage.
Pi (max) = the maximum active power (MW) capacity of line i and it is given by DC load flow
analysis as:
ij
ji
i
X
VV
P
max
2
Vi = voltage at Bus i after the completion of the N-R load flow analysis
Vj = voltage at Bus j after the completion of the N-R load flow analysis and
Ω
∑
∑
∑
∑
Ω
Ω
Ω
4. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 15
Xij = the reactance of the line connecting lines i and j.
NL = is the number of transmission lines in the system under consideration
W and r are real non negative weighting factor and exponential penalty factor respectively.
W = 1 and r = 2 are said to be adequate for 14Bus system (Javan et. al, 2011).
It should be noted that according to Javan et. al, (2011); the Active power performance (PIA) has
a small value when all the line flows are within their limits and has a high value when any
line(s) is (are) overloaded for a given state of the power system.
2.1.2 Reactive Power Performance Index
The Reactive Power performance Index (PIR) is a function of Bus voltage limit violations and it
is given by:
r
ii
avii
N
i
R
VV
VV
r
W
PI
LB
2
(min)(max)1
2
2
3
where
Vi = Voltage at bus i
Vi(max) and Vi(min) are the maximum and minimum voltage limits.
2
(min)(max)
)(
ii
avi
VV
V
NLB = No of load Buses in the system.
W = Weighting coefficient
r = Order of the exponent
It should be noted that the voltage range used is +10% and -10% according to Transmission
Company of Nigeria (TCN), Osogbo) as reported by Onojo et. al., (2013), hence at slack/ nominal
voltage of 1.0p.u, there will be a violation if either the Vi(max) or Vi(min) is above 1.05p.u or below
0.95p.u (1.10p.u or 0.9p.u in Transmission Company of Nigeria's case) respectively. Reactive
power performance index (PIR) is therefore an indication of the severity of the contingency
(outage) on a particular line and if it is greater than zero, the corresponding contingency is
recognised as critical or insecure otherwise it is said to be Secure.(Javan et. al., 2011).
A Matlab based code was deployed for the actualisation of this Analytical method using the flow
chart of Figure 2.
5. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 16
Carry out the reference load
flow
Are all lines
outages simulated
?
Supply
input(network
lines and buses
data)
Start
Simulate contingency
Calculate PIA using equation 1
and 2
Initial counter
Perform N-R load flow analysis
Calculate PIR at all load buses
using equation 3
Rank contingency as a function
of PIR and PIA
Print
Ranked PIR
and PIA
Stop
Figure 2: Flow Chart for Contingency
Analysis Using Analytical Method
Divide the input data to Training
and Testing
Is | RBFPIR – AMPIR | EMR and
Is |RBFPIA –AMPIA | EMA
Supply the RBF_NN inputs [x]
= [PG1 QG1...PGn, QGn, PL1,
QL1...PLn, QLn, V1...Vn]
Train RBF neural network
Compute last hidden units
output
Set values for SSE, Spread
Constant, EMR and EMA
Simulate the model Testing
Rank contingency as a function
of RBFPIR and RBFPIA
Print
Ranked
Result
Stop
Figure 3: Flow Chart for Contingency
Analysis Using RBF-NNl Method
Simulate the N-1 line contingency of the system
and compute the AMPIA and AMPIR
Is
MSE SSE
?
Start
Yes
No
No
Yes
Yes
No
6. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 17
2.2 RBF-NN Method
The contingency analysis using Analytical method in view of the large number of components in
the power system, is slow hence the use of Radial Basis Function Neural Network with the flow
chart in Figure 3 was used in this study. The procedure of RBF-NN method involves feeding the
result of load flow following each contingency into the developed RBF-NN algorithm to compute
and rank the performance indices.
Contingency Analysis of the IEEE-30 Bus Standard system and TCN-28 Bus of Nigerian system,
using Newton-Raphson (N-R) for load flow analysis and RBF-NN prediction method to determine
the performance indices were carried out. This was achieved using Matlab code based on the
flow chart in Figure 3 which was based on the general structure of RBF-NN in Figure 1 with the
determination of hidden neurons based on an algorithm called Growing and Pruning algorithm
reported by Javan et al, 2011. The statistical data used for each system is as shown in Table 1.
S/No Power
Systems
No. of
Lines
Error
Goal
Spread
Constant MSE SSE
Correlation
Coefficient
(R)
1 IEEE-30 Bus (Normal) 41 1.00E-03 15 1.001 1.0005 0.8914
2 IEEE-30 Bus (Improved) 44 1.00E-04 15 1.0331 1.0005 0.7496
3 TCN-28 Bus (Normal) 52 1.00E-03 20 0.006 0.0778 0.945
4 TCN-28 Bus (Improved) 53 1.00E-04 20 0.0081 0.0902 0.9727
TABLE 1: The statistical data of RBF-NN Method.
The Line diagram of TCN-28 system is as shown Figure 4 while that of standard IEEE-30 Bus
could be easily gotten online via www.ieee.org.
7. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 18
Okpai 27,
V27 = 1.050pu
Calabar 25,
V25 = 1.049pu
Alaoji 12,
V12 = 1.035pu
Afa G.S 11,
V11 = 1.050pu
Aladja 7,
V7 = 1.040pu
Delta G.S 2,
V2 = 1.050pu
Sapele G.S 24,
V24 = 1.050pu
Onitsha 14,
V14 = 1.050pu
New Haven 13,
V13 = 0.977pu
Papalanto G.T
28,
V28 = 1.050pu
Ikeja west 5,
V5 = 1.026pu
Egbin 1,
V1 = 1.026pu
Aja 3,
V3 = 1.045pu
Akangba 4
V4 = 1.019pu
Ayede 9,
V9 = 0.990pu
Osogbo 10,
V10 = 1.031pu
Benin 8,
V8 = 1.029pu
Ajaokuta 6,
V6 = 1.029pu
Abuja26,
V26 = 1.026pu
Jebba G.S 17,
V17 = 1.050pu
Jebba T.S 18,
V18 = 1.050pu
Shiroro G.S 23,
V23 = 1.050pu
B. Kebbi 15,
V15 = 1.065pu
Kanji G.S 21,
V21 = 1.050pu
Kaduna 20,
V20 = 1.040pu
Kano 22,
V22 = 1.010pu
Jos 19,
V19 = 1.051pu
Gombe 16,
V16 = 0.994pu
Figure 4: TCN – 28 Bus Nigerian System Pre-contingency Load Flow
(Source: TCN , 2013)
3. RESULTS AND DISCUSSION
The simulation of Contingency analysis of the two systems were carried using RBF-NN method
with normal data and improved system data and the result are presented as follows;
3.1 Standard and Improved IEEE-30 Bus Systems
Upon the implementation of the RBF-NN method for the Standard IEEE-30 Bus system, Lines 13,
16 and 34 were found not to have converged as shown in Figures 4a, these lines were ranked as
39th, 40th and 41st most critical lines respectively hence they were regarded as the least critical
lines while the three most critical lines are Lines 36, 37 and 38; based on the fact that they have
largest number of voltage violations ranging from 1.082 to 0.861pu. These results were found to
be in agreement with the reports of Mario and Carlos, 2003 and Sarika et. al., 2013. The non-
convergence of the above lines was however an indication of their criticality to the functionality of
8. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 19
the system hence outage of any of those three lines could lead to instability and eventually the
shutdown of the system. They were therefore reinforced with lines of the same parameters.
The simulation of this improved System gives the result ranked in Figures 4b., where the most
critical line is 39 (the initial line 36) and the least critical line 15 (former line14).
FIGURE 4a: Contingency Analysis Ranking of Standard IEEE-30 Bus System using Reactive performance
indices.
FIGURE 4b: Contingency Analysis Ranking of Improved IEEE-30 Bus System using Reactive performance
indices.
3.1 Standard and Improved TCN-28 Bus Systems
Also the implimentation of the method on the real Nigerian transmission TCN-28 Bus System
gives the resulting Reactive and Active performance indices shown in Figure 5a(I and II)
respectiely; which indicated that line 31 did not converge, hence was considered to be the least
9. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 20
critical line. Upon reinforcement, line 31 and the incorporated line 32 were still ranked to be the
least critical lines as far as voltage violation is concerned but this was found not to be so based
on power violation, since the most critical line was line 20 as against the initial line 31 in the
standard TCN-28 Bus system. The change in the most critical line from 31(in normal system) as
against to line 20 (in improved system) is an indication of imperfect ranking going by the
contingency analysis of the normal system. This is shown in Figure 5b (I and II). Also, the least
critical line after the reinforcement were lines 29 and 30.
FIGURE 5a(I): Contingency Analysis Ranking of Standard TCN-28 Bus System using Reactive
performance indices.
FIGURE 5a(II): Contingency Analysis Ranking of Standard TCN-28 Bus System using Active performance
indices.
10. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 21
FIGURE 5b(I): Contingency Analysis Ranking of Standard TCN-28 Bus System using Reactive
performance indices.
FIGURE 5b(II): Contingency Analysis Ranking of Standard TCN-28 Bus System using Active performance
indices.
4. CONCLUSION AND RECOMMENDATION
4.1 Conclusion
This research investigated the proficiency of artificial intelligent; radial basis function neural
network for revealing the effect of non-convergent power flow on the contingency analysis of
electrical power systems by predicting the Active and Reactive performance indices and ranking
both in ascending order to show the severity of the transmission lines outages based on power
and voltage violations respectively. The contingency analysis of lines with non-convergent power
flow which were ranked least were reinforced and their criticality as far as the stability of the
system either as a result of voltage or power flow violations is concerned were revealed.
11. Taofik Adekunle Boladale & G. A. Adepoju
International Journal of Engineering(IJE), Volume (10) : Issue (2) : 2016 22
Following the above findings it could be concluded that, non-covergency of some of the lines of
any power system will definitely affect the contingency analysis of such system as any outage of
the non convergent line(s) could lead to the instability and shutdown of such system since either
the voltage or power of the particular neighbouring components could be easily driven beyond the
operating limits.
Consequently in order to appropriately analysed any system with such non-convergent line(s),
reinforcing such will give the true picture of system.
Lastly, incorporation of parallel line(s) in real power systems such as TCN-28 Bus system will
improve the stability property of the system aside providing additional capability of power
transmission.
4.2 Recommendation
This research work concentrate on N-1Line contingency, multiple components contingency with
RBF-NN especially on large real systems should be considered in future work.
5. REFERENCES
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