Search

In deregulated power systems, reactive power ancillary service through electricity market is becoming relevant where private generation companies participate in maintaining system wide bus voltage within the permissible limits. Marginal cost price (MCP) based real time reactive power ancillary service market faces several challenges due to the localized nature of reactive power. In this paper, a market mechanism for real time reactive power ancillary service market based on Stackelberg game model is proposed considering voltage-apparent power coupled subsystems. In the proposed Stackelberg game model, Independent System Operator (ISO) is considered as the leader, and GENCOs as followers. In the formulation, each GENCO is associated with a relevance factor in the partitioned subsystem so as to consider the real time voltage support requirement in the system. The market is then formulated as Mathematical Program with Equilibrium Constraints problem (MPEC). Existence of equilibrium, incentive compatibility, and individual rationality of the proposed market mechanism is then analysed in this work. The numerical examples are illustrated in PJM 5-bus system, and tested on IEEE 30- bus system, and Nordic 32 Bus-system. The mechanism induces truth-telling behavior of GENCOs, yields a non-negative profit, and the system wide bus voltage is improved.

The optimal protection coordination (OPC) problem of directional overcurrent relays (OcR) in power system networks is a complex, highly constrained nonconvex optimization problem because of the non...

This work proposes a novel relative electrical distance measure that provides information of coupling between voltage and apparent power between two buses in power systems. Relative electrical distance measure is derived from the bus admittance matrix which can be obtained in real time using Phasor Measurement Units. Based on the relative electrical distance measure, in this work, an isoperimetric clustering based algorithm for partitioning power systems into voltage–apparent power coupled areas is proposed. The advantage of the partitioning algorithm proposed in this work is that large networks can be represented as a weighted graph with number of vertices equal to number of generators in the system which is much lesser than the size of system, thereby reducing the computational effort for partitioning. Isoperimetric clustering technique along with k-means is then applied to the graph to obtain voltage–apparent power coupled areas. Simulations carried out on New England 39-bus system and IEEE 118-bus system demonstrate the effectiveness of the proposed methodology for partitioning the system into voltage–apparent power coupled areas, subject to changes in the operating condition of the system. The quality of clustering is analysed and compared with Cheeger inequality bounds, which ensures that power system is well partitioned.

This paper proposes a robust fully distributed consensus based algorithm (RFDCA) for optimal dispatch of generating units in power systems with a practical communication network, which considers noise, communication delay and a dynamically switching communication topology. The proposed approach is based on double consensus $- \lambda$ consensus achieved through first-order consensus protocol and $\Delta P$ consensus through average consensus protocol. The adverse effects of noise and communication delay can be lessened through a monotonically decreasing consensus gain function. The main advantage of this method is that the generating units need not share their cost characteristics to anyone, thereby preserving their private information. Another advantage of the proposed approach is that it can deal with networks of different sizes and topology, and by choosing the proper convergence coefficient, a faster rate of convergence can be achieved. Simulations are carried out on standard IEEE test systems to illustrate the efficacy of the RFDCA approach on both smaller and larger systems.

This paper is a review of uncertainty modeling techniques used in smart grid studies. The literature dealing with uncertainty from various sources in smart grid is analyzed and presented. In a modern power grid, the risk may arise due to different reasons; in-termittent renewable energy sources, uncertain consumer reactions on demand response, driving patterns of electric vehicles, etc. The paper has two objectives. First is to bring out the trends in uncertainty handling techniques used in electrical power system problems, and second to introduce the scope of new risk processing techniques with the perspective of recent smart grid issues.

This paper analyses the False Data Injection Attack (FDIA) on tie-line sensor measurements of a Load Frequency Control (LFC) System from an attacker’s perspective. The attack vector has been modeled considering an advanced LFC system, wherein, the measurements undergo State Estimation and bad data detection algorithms. Two different attack vector modeling scenarios have been considered: in the first model, an arbitrary attack vector modeling is performed to study its effect on the system operation; The later model is built systematically to induce sequential outages. The significant advantage of the proposed method is that the number of measurements to be attacked and system knowledge like topology data, line parameter values, transfer function parameters that are required is very low. The results are studied for an IEEE 39 bus, 3 area system.

Modern power system is moving towards a smart and competitive grid, with competing generating companies, power retailers, and strategically behaving consumers playing a crucial role in the daily operation of the system. Independent System Operator (ISO) monitors these daily operations and procure required services through market operations. Active and reactive power pricing in real-time at the wholesale and retail level is considered an efficient energy management method. However, procurement of reactive power through a market mechanism in real-time is not being implemented, despite its crucial role in maintaining system parameters within the permissible limits. This paper attempts to detail the challenges faced in implementing such reactive power markets and presents a review of existing reactive power market mechanisms to address these challenges. A framework suitable for reactive power ancillary service in a smart grid is also detailed in this paper.

An iterative distributed algorithm is proposed for existing power system optimization problems. Here the centralized optimal dispatch problem is explained in a decentralised manner. The line flow constraints along with generation limits are distributed into different nodes of the given network. In order to achieve a globally optimal solution, the incremental cost and the penalty factor for congestion are varying iteratively. The distributed algorithms provide flexibility, robustness, and scalability. The algorithm is tested for optimal power dispatch in an IEEE 39-bus system and welfare maximization problem in a 3-bus system. The results are validated with the standard centralised approach.

The increased integration of Distributed Energy Resources (DER) and intelligent control technologies to distribution networks have succeeded in framing foundation for smart power grids. Design of protection systems and voltage regulation methodologies for an interactive smart grid will face new challenges with the introduction of new grid codes, active network management schemes and islanded operation. The active network management and optimal sizing of distributed sources shall change the operating point of the smart inverter-based DER in the islanded electric power system (EPS) continuously. This paper assesses the impact of such schemes on fault current magnitudes for various fault cases and voltage regulation at various nodes of an islanded EPS with distributed energy resources.

In the present electricity market, where renewable energy power plants have been included in the power systems, there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving the unit commitment (UC) problem. Dynamic programming (DP) is a conventional algorithm used to solve the deterministic problem. In this paper DP is used to solve the stochastic model of UC problem. The stochastic modeling for load and generation side has been formulated using an approximate state decision approach. The programs were developed in a MATLAB environment and were extensively tested for a four-unit eight-hour system. The results obtained from these techniques were validated with the available literature and outcome was good. The commitment is in such a way that the total cost is minimal. The novelty of this paper lies in the fact that DP is used for solving the stochastic UC problem.

Reactive power plays a prime role in power system operation. But contingencies due to reactive power shortages are rarely considered while evaluating the reliability of a system. This paper takes into account the failure of reactive power sources also while evaluating reliability of a system and calculates a separate set of reliability indices due to reactive power shortage. Demand Side Management (DSM)is a promising attempt in improving the system reliability. Here we consider the novel concept of including the effect of reactive power failures in DSM reliability studies. Reliability indices are evaluated for the modified IEEE 30 bus system and also for the same one where DSM is already in operation, which makes it a smart grid. We see that the reliability is improved in the latter case even on considering reactive power source failures also.

It is important to handle the transmission network expansion planning problem under restructured power systems to allow fair competition in the system. A scenario-based two-stage Transmission Network Expansion Planning (TNEP) is presented in the paper and solved using a meta-heuristic algorithm. Various scenarios, for market-driven power flow patterns is considered in a 6-bus Garver Test System, and Differential Evolution is employed to solve the traditional Transmission Network Expansion planning problems in each scenario. A decision making scheme based on risk analysis is employed to select a plan with minimum risk. In this paper it is assumed that the market players trade only via pool market model, where bid pricing affects the generator loading.

Power system is a large-scale network with a number of components and interconnections for which centralized control becomes cumbersome. For multi-area computations, decentralization is necessary. For implementation of this approach network decomposition becomes an essential task. In this paper the network decomposition problem is solved as an optimization problem using the harmony search (HS) algorithm. To improve the performance of the HS algorithm, a widely used graph bi-partitioning method called Kernighan-Lin (KL) strategy is used in the improvisation process. KL strategy is used in the partitioning of digital and VLSI circuits and is suitable for bi-partitioning networks. The connectivity of the partitioned clusters are checked by means of graph traversal techniques. Simulation are carried out on IEEE Standard systems and found to be very effective in decomposition of the system hierarchically. � 2012 Elsevier B.V. All rights reserved.

Power system has a highly interconnected network that requires intense computational effort and resources for centralized control. Distributed computing needs the systems to be partitioned optimally into clusters. The network partitioning is an optimization problem whose objective is to minimize the number of nodes in a cluster and the tie lines between the clusters. Harmony Search(HS) Algorithm is one of the recently developed meta heuristic algorithms that can be applied to optimization problems. In this work, the HS algorithm is applied to the network partitioning problem and power flow based equivalencing is done to represent the external system. Simulation is done on IEEE Standard Test Systems. The algorithm is found to be very effective in partitioning the system hierarchically and the equivalencing method gives accurate results in comparison to the centralized control. � 2012 Elsevier Inc. All rights reserved.

This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques.

The paper proposes a multi-objective biogeography based optimization (MO-BBO) algorithm to design optimal placement of phasor measurement units (PMU) which makes the power system network completely observable. The simultaneous optimization of the two conflicting objectives such as minimization of the number of PMUs and maximization of measurement redundancy are performed. The Pareto optimal solution is obtained using the non-dominated sorting and crowding distance. The compromised solution is chosen using a fuzzy based mechanism from the Pareto optimal solution. Simulation results are compared with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Non-dominated Sorting Differential Evolution (NSDE). Developed PMU placement method is illustrated using IEEE standard systems to demonstrate the effectiveness of the proposed algorithm.

The paper presents a biogeography based optimization algorithm to design an optimal meter placement scheme which makes the power system network observable. The procedure consists of initially determining the optimal meter set under normal conditions, followed by the optimal reliable meter locations that are obtained under two types of contingencies termed as single meter failure or loss and single branch outages. This is achieved by modifying the derived scheme from the normal condition using penalty functions. The effectiveness of the meter placement algorithm is demonstrated for IEEE standard systems. � 2011 Elsevier B.V. All rights reserved.

Security is recognized as an important problem in planning, design and operation stages of electric power systems. Power system security assessment deals with the system's ability to continue to provide service in the event of an unforeseen contingency. This paper proposes a particle swarm optimization (PSO) based classification for static security evaluation in power systems. A straightforward and quick procedure is used to select a small number of variables as features from a large set of variables which are normally available in power systems. A simple first order security function is designed using the selected features for classification. The training of weights in the classifier function (security function) is carried out by PSO technique. The PSO algorithm has minimized the error rate in classification. The procedure to determine the security function (classifier) is discussed. The performance of the algorithm is tested on IEEE 14 Bus, IEEE 57 Bus and IEEE 118 Bus systems. Simulation results show that the PSO classifier gives a fairly high classification accuracy and less misclassification rate. � 2010 Elsevier B.V. All rights reserved.

Modeling and simulation of nonlinear systems under chaotic behavior is presented. Nonlinear systems and their relation to chaos as a result of nonlinear interaction of different elements in the system are presented. Application of chaotic theory for power systems is discussed through simulation results. Simulation of some mathematical equations, e.g. Vander Pol's equation, Lorenz's equation, Duffing's equation and double scroll equations are presented. Theoretical aspects of dynamical systems, the existence of chaos in power system and their dependency on system parameters and initial conditions using computer simulations are discussed. From the results one can easily understand the strange attractor and transient stages to voltage collapse, angle instability or voltage collapse and angle divergence simultaneously. Important simulation results of chaos for a model three bus system are presented and discussed. � 2010 Elsevier B.V. All rights reserved.

Differential evolution based optimal reactive power dispatch for real power loss minimization in power system is presented in this paper. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts to be switched, for real power loss minimization in the transmission system. The problem is formulated as a mixed integer nonlinear optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling. The formulation also checks for the feasibility of the optimal control variable setting from a voltage security point of view by using a voltage collapse proximity indicator. The algorithm is tested on standard IEEE 14, IEEE 30, and IEEE 118-Bus test systems. To show the effectiveness of proposed method the results are compared with Particle Swarm Optimization and a conventional optimization technique - Sequential Quadratic Programming. � 2007 Elsevier B.V. All rights reserved.

Traditional Load Flow methods are considered as one of the most important tools for analyzing the operation of power system and to perform the planning studies. Since distribution systems exhibit characteristics like higher R/X ratio and loading of the feeders is higher, traditional load flow methods fail to analyze these networks and hence separate methods are developed to handle the situation. However, these distribution load flow methods restrict to calculating power flow at feeder level (> 11 kV) by representing load as a suitable model. The load segments i.e. 3-phase 4-wire LV network downstream to the 11kV/400V distribution transformer, is represented as time deterministic static loads while calculating distribution load flow. Due to the increase in demand for more situational awareness until the edge of the network and with large scale deployment of Advanced Metering Infrastructure (AMI) & Geographical Information System (GIS) technologies, it is possible to do load flow analysis on 400V 3-phase, 4-wire LV distribution network in phase-wise manner using smart meters, geographical & communication technologies. In this paper, we propose algorithm for such a load flow method and discuss its applications.

With the increasing adoption and implementation of IEC 61850 based substation automation systems (SAS), it has now become very important for power system engineers to know and understand technologies outside the realm of regular power system concepts which are not taught until graduate studies. Present day tools and systems are either costly and proprietary in nature or, are open source but not easy to understand and use them to implement and test protection and automation schemes based on International Electro technical Commission (IEC) 61850 in a lab based environment. Taking this as motivation, an IEC 61850 based laboratory prototype is developed using royalty free and free and open source software (FOSS) for modeling the power system and the distance relay. In order to show the flexibility of the proposed framework, a complex and vital protection scheme known as local breaker backup (LBB) which has been recently integrated into a centralized bus bar protection scheme is implemented and tested. Personal computers are used as merging unit (MU) and intelligent electronic devices (IEDs), and, Ethernet based local area network (LAN) and switches have been used for implementing process bus and station bus. This cost effective prototype can be used by the academia and engineers to study, understand, implement and test various IEC 61850 based protection and automation schemes.

This paper presents a robust and optimal metering systems and optimal Phasor Measurement Unit (PMU) placement for real time power system monitoring taking into account of both normal and contingency cases. Two types of contingencies are named as single branch/meter outage and single branch/PMU outage for optimal meter and PMU placement respectively. The placement of meter and PMU are solved by Biogeography Based Optimization. In the PMU placement study, the virtual bus reduction technique is applied for reducing the scale of the system. Developed placement algorithm is illustrated using IEEE standard systems to demonstrate the effectiveness of the proposed algorithm.

This paper presents an approach of analyzing power system voltage stability based on system potential. This framework provides a simple and better way of understanding voltage stability by visualizing the changes in topological structure of the system potential with respect to the change in power system loading. This paper also investigates the effect of random load variation in the power system stability. The stability margin index being studied is the Mean First Passage Time of the power system from the stable operating point to the closest unstable equilibrium point for small magnitude random load perturbations. The variation of MFPT for change in the loading of the system, Intensity and correlation time of the stochastic load are studied initially for an SMIB system. � 2011 Elsevier Ltd. All rights reserved.

Security assessment is a major concern in planning and operation studies of a power system. Conventional method of security evaluation performed by simulation involves long computer time and generates voluminous results. This paper presents a K-means clustering approach for classifying power system states as secure/insecure under a given operating condition and contingency. This paper demonstrates how the traditional K-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The proposed algorithm combines particle swarm optimization (PSO) with the traditional K-means algorithm to satisfy the requirements of a classifier. The proposed PSO based K-means clustering technique is implemented in IEEE 30 Bus, 57 Bus, 118 Bus and 300 Bus standard test systems for static security and transient security evaluation. The simulation results of the proposed algorithm are compared with unsupervised K-means clustering, which uses different methods for cluster center initialization. � 2011 Elsevier Ltd. All rights reserved.

Security assessment is an important concern in planning and operation studies of an electric power system. Conventional method of security evaluation is performed by simulation consisting of load flow program and transient stability analysis, consuming long computer time and generating voluminous results. This paper presents a practical Pattern Recognition (PR) approach for security assessment in power systems. The problem of security assessment is focused in two modes, viz., static and transient. Static security pertains to the study of violation of system components when subjected to contingencies like line/generator outages. Transient Security study deals with system dynamic behavior when subjected to severe perturbations like three phase faults. A Supervised Fuzzy C-Means (SFCM) algorithm is proposed in the classification phase of PR system for security assessment. The proposed algorithm is tested on 39 Bus New England and IEEE 57 Bus systems. The classification results of the proposed SFCM classifier is compared with the Method of Least Squares (MLS) and Multilayer Perceptron (MLP) classifiers. The results prove that the former gives high classification accuracy and less misclassification rate compared to the latter, enhancing the feasibility and applicability of SFCM algorithm for on-line security evaluation. Keywords: Security Assessment, Static Security, Transient Security, Pattern Recognition, Fuzzy C-Means.

A new generation rescheduling approach for preventive control of power system is presented which can reallocate power generation for multiple unstable contingencies based on graph theory. The objective of preventive control is to prepare the system when it is still in normal operation, so that the system is able to face future uncertain events in a satisfactory way. The generator rescheduling is based on the outage of links from one generator area to the other without violating line overloads. The quantity of generation shifting from one generator area to other is calculated by the proportionality assumption. When rescheduling of generators fails to provide a feasible solution for secure operation, optimal load shedding/switching off disturbed area, called islanding formation, is resorted to. The suitability of the proposed method is demonstrated with the help of six bus three machine power system and IEEE 30-bus power system. � 2009 Elsevier Ltd. All rights reserved.

State estimator is an important tool for online monitoring, analysis and control of power systems. With the increased use of phasor measurement units in a power grid, utilization of synchro phasor measurements in State Estimation (SE) becomes essential. Conventional State Estimation (CSE) has been done from measurements obtained by Supervisory Control And Data Acquisition (SCADA) systems. Linear State Estimation (LSE) is done using phasor measurements alone. The paper presents a hybrid method called Two Stage State Estimation (TSSE), wherein the states of the system are estimated by incorporating both the SCADA measurements and phasor measurements. Simulations are done on IEEE 14 bus, IEEE 30 bus and IEEE 118 bus systems and results are compared.

This paper presents an implementable distributed load flow calculation method for online analysis, for power systems having multiple, geographically separated areas. The system is partitioned into multiple areas, with each area having its own control centre. Adjacent areas share a tie-line and the power flowing in these tie-lines are expected to be known to the control centres concerned before the analysis is started. Since each area may be governed by different companies, it is justified to expect that the power that is traded through a tie-line would be known to the control centres sharing the line. The proposed method uses the fast decoupled load flow approach and it suitably adds elements to the B' and B" matrix to find out the state variables of the boundary buses. Results on the IEEE 14 and 30 bus systems show that the distributed analysis converges as fast as the centralized ones.

Detection and identification of a power system emergency is necessary to fully utilize the capability of controls to mitigate emergencies. A new classification scheme of power system emergencies and its operating states is presented. Linear sensitivity and D-factor methods are used to identify the overloading of the power system components. Transient stability analysis is used to identify the emergency operating states of power system. Classification and detection of emergencies has been useful for developing emergency operating state controls. Test result shows the use of performance index and transient stability study as suitable for efficient identification of the emergency state of a power system.

This paper presents differential evolutionary algorithm for optimal dispatch for reactive power and voltage control in power system operation studies. The problem is formulated as a mixed integer, nonlinear optimization problem taking into account both continuous and discrete control variables. The optimal setting of control variables such as generator voltages, tap positions of tap changing transformers and the number of shunt reactive compensation devices to be switched for real power loss minimization in the transmission system are determined. In the proposed method, the inequality operational constraints were handled by "penalty parameterless" approach. This helps in avoiding the time-consuming trial and error process for fixing the penalty parameter and makes the process system independent. The algorithm was tested on standard IEEE 14,30,57 and 118-Bus systems and the results compared with conventional method. � 2008 Elsevier Ltd. All rights reserved.

Static security assessment and analysis using external system (Ward network) equivalent is presented. Contingency analysis is performed on the reduced network obtained using extended Ward equivalent. The Ward equivalent provides reasonably good results for contingency studies involving active power flows. Distribution factors method is used to perform contingency analysis. Fast line flow contingency screening is implemented for both the original unreduced network and the reduced network. The real power performance index (PIMW) that reflects the degree of severity of contingencies is calculated using the screening method and full AC load flow method. The effectiveness of the proposed method is demonstrated by performing contingency analysis on a standard 6-bus system, IEEE 14-bus, IEEE 30-bus and IEEE 57-bus systems, respectively. The performances of the reduced network and the screening method are compared with the traditional Newton-Raphson (NR) method and the results are discussed. The external network equivalent approach to security analysis was found to be suitable for fast line flow contingency analysis. � 2007 Elsevier Ltd. All rights reserved.

Effective power system operation requires engineers and operator to analyze large amounts of multivariate data. Power system studies have a need for effective visualization software to display large amounts of information on the screen. The visualization tools encountered by many power system operators and engineers have evolved little beyond tabular displays and the one-line diagram. Java-based visualization of power system operation is presented. In addition to the single line diagram, various new approaches including contouring, addition of dynamically sized components, adding an extra dimension to the plot using various 3D Application Programming Interfaces (APIs), such as OpenGL are described. Two case studies of Economic Energy Interchange in a Power Pool with Energy brokerage and load flow are considered to illustrate the working of the proposed technique.

This paper presents a detailed report of the performance and impact of UPFC on the power system behavior during fault conditions. A two machine — double line power system with UPFC installed in one of the lines is considered for the study. The idea is to observe the capability of the UPFC to maintain the active and reactive power flows in the compensated line (which includes UPFC) and to diminish the fall-off of the bus voltage when there is a grounding fault in the transmission line. Simulink based power system block set is used for numerical simulations. Simulation results indicate notable improvement in the behavior of the overall system with UPFC in upholding the voltage and power flows even under typical line faults by appropriate injection of series voltage into the transmission line at the point of connection. The extent of UPFC capability in maintaining the power flows in the line (Line 1), even under fault condition in an adjacent parallel line (Line 2) is provided. Minimizing the disturbances in voltages, currents and power flows in the fault-affected line (Line 2) are also discussed. Further the results illustrate how the UPFC contributes dynamically to a faster recovery of the system to the pre-fault conditions.

Decision tree approach for security assessment and evaluation is presented. The general decision tree methodology is proposed for the purpose of predicting the robustness of a power system in the occurrence of severe disturbances, and of discovering appropriate control actions whenever needed. The decision trees are built off-line and are used on-line. Their building calls upon an inductive inference method, which automatically extracts the relevant information from large bodies of simulation data and express in terms of the system controllable variables. In this paper decision trees are used for classifying the operating states of the power systems as either secure/insecure using the MW flows and voltages and the severity level of the violation and the control actions necessary by finding the location of operating point when dropped at the top node of the decision tree.

Artificial neural networks (ANN) based pattern recognition for steady state (SSA), transient (TSA) and dynamic security assessment (DSA) of the power systems is presented. Conventional methods for security assessment take lot of time to assess the security of power systems when all contingencies are considered. Artificial neural networks based pattern recognition is used to solve SSA, TSA and DSA and its effectiveness against conventional methods is presented. In addition contingency ranking and generator severity ranking is used to measure the severity of various contingencies which allows an operator to take preventive control (generator rescheduling) or to be ready for corrective actions. Direct methods are fast compared to numerical methods but proper choice of Lyapunov function is difficult. ANN based PR for SSA, TSA and DSA effectiveness is compared with other conventional methods like pattern recognition which confirms that ANN not only gives accurate results but also is adaptive to the topology changes of the power system. In addition contingency ranking and generator severity ranking has been addressed which allows the operator to take preventive control actions like generator rescheduling.

Energy management engineers are focusing their interest in tapping maximum profit for their system from substation automation (SSA)/distribution automation (DA). Volt/Var control through fixed/switched capacitors, transformer taps and voltage set points are at different levels of research and implementation. A neural network based solution for voltage-VAR control is proposed with the aim to reduce the real power loss flowing in a power system and subsequently improve the voltage profile. The module consists of two networks. The first network determines the control parameters i.e., generator voltage, transformer taps and shunt capacitance for minimal power loss when the loads at the load buses are specified as inputs. With the obtained parameters, a load flow program is run and power loss is noted and the system is checked for voltage violations. In case of voltage violations, the voltages are fed to the second network, which gives dQ at different buses for voltage violation minimization. These modules are successfully tested for different load patterns on a six-bus system.

Power system computation software needs revision of its design due to modernization of existing network and automation of the power system operation. Object-oriented design has gained wide spread importance and acceptance in software development due to its advantages over other methodologies. Large-scale power system computation involves sparse matrices. External system equivalents are widely used to reduce computation complexity. This paper proposes an object-oriented design of sparse matrices and field proven Paul Dime's REI equivalent (radial equivalent independent of other nodes). 'Dynamic binding' and 'polymorphism' of object-oriented design have been exploited in this work to develop the software for REI equivalent. The object-oriented design is implemented using C/sup ++/ programming language and tested with 6-bus, IEEE 14-bus and IEEE 30-bus systems. Results for the IEEE 14 bus system in the form of bus admittance matrix using single load REI equivalent, single generator REI equivalent and single generator single load REI equivalent are illustrated.

The controlling functions in the SCADA/EMS system are increasing with the expansion of the power system network as the demand for power increasing. It is difficult to control the functions with the centralized system and rely on a single vendor. Current technologies employ distributed systems that are open (i.e. vendor independent solution) to control the SCADA/EMS functions. With open systems utilities, it tends to incrementally upgrade rather than completely replace the EMS systems. Various open distributed architectures have been proposed to shoulder the SCADA/EMS functions. This paper introduces the concept of open system and examines one of the distributed hardware configurations for open EMS. The proposed work modeled the above contingency analysis procedure for a case study of 6 bus, 11 line network, which was done by a dedicated application server on the pre-specified port.

A new formulation of the power system state estimation problem and a new solution technique is presented. The formulation allows for inequality constraints such as generator VAr limits and transformer tap ratio limits. The solution technique uses a logarithmic barrier function to treat inequality constraints. Non-quadratic criteria for power system state estimation have to facilitate bad data detection and identification. The weighted least absolute value criteria have the desired bad data rejection property. This paper shows that the logarithmic barrier function can accommodate inequality constraints, and then applying the Newton method to solve the KKT equation leads to a problem structure which is identical to that of the non-linear least square problem. The number of iterations needed for convergence does not increase linearly with problem size, unlike the LP algorithm.

A new method for the solution of the problems of unit commitment (UC) and economic dispatch (ED) using Hopfield neural network (HNN) is proposed in this paper. The difficulty in combining these problems is that while the first one requires a discrete neuron model, the latter requires a continuous neuron model. The combined solution of these problems using HNN requires the interconnection of discrete and continuous neural network models and the formulation of a unified energy function, which is quite complicated. The important contribution of this work is the proposal of a new architecture for the discrete HNN for UC and the output of the UC module is used as input to the continuous HNN for ED. The associated advantage of using HNN for the combined solution of UC and ED is the decoupling of their interdependency i.e:, both the UC and ED are iteratively solved using respective HNN for the particular period. The implementation of the proposed method causes a considerable reduction in the HNN size and hence complexity and computation requirements, compared to earlier attempts. The method was successfully tested for different cases (3,10,11 and 26 generator units), with varying load pattern of different durations (24 and 168 hours).

This paper presents the genetic algorithm approach to adaptive optimal economic dispatch of electrical power systems. Genetic algorithms, also termed as the machine learning approach to artificial intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Genetic algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The unique problem solving strategy of the genetic algorithm and their suitability for power system optimization is described. The advantages of the genetic algorithmic approach in terms of problem reduction, flexibility and solution methodology are also discussed. The suitability of the proposed approach is described for the case of a 15 generator power system. >

Large interconnected power systems with dispersed and geographically isolated generators and load constitute a majority of the power network. Present-day power systems are dynamic in nature, where the network topology frequently changes with load demand. With increase in load, the power system network is loaded to its limits, making it susceptible to collapse even under minor disturbances. In order to operate the power system economically, the current operating state of the system must be identified as either secure or insecure. An artificial neural network (ANN) aided method for security assessment is proposed and illustrated for a model six-bus power system. The work demonstrates the feasibility of classification of load patterns for power system static security assessment using a Kohonen self-organizing feature map. The most important aspect of this network is its generalization property. Using 15 different line-loading patterns for training, the network successfully classifies the unknown loading patterns. This powerful and versatile feature is especially useful for power system operation. Research is in progress to include contingency analysis in the security assessment program.

This paper presents a new multiagent-based differential evolution (MADE) approach to the reactive power dispatch problem. Reactive power dispatch problem can be formulated as a mixed-integer non-linear optimisation problem. The proposed method integrates multiagent systems (MASs) and differential evolution (DE) algorithm. An agent in MADE represents an individual to DE and a candidate solution to the optimisation problem. All agents live in a lattice like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbours and it can also use knowledge. Making use of these agent-agent interaction and DE mechanism, MADE realises the purpose of minimising the value of objective function. In this study, the proposed MADE, DE and PSO are applied to minimise the transmission losses on standard IEEE 14, 30 and 57 bus test systems. Simulations results are compared with DE, PSO and existing methods reported in the literature. The results show that the proposed method converges to better solutions much faster than the earlier methods. Copyright � 2013 Inderscience Enterprises Ltd.

Real time fault detection and diagnosis (FDD) is an important area of research interest in knowledge based expert systems. Neurocomputing is one of fastest growing areas of research in the fields of artificial intelligence and pattern recognition. The authors explore the suitability of pattern classification approach of neural networks for fault detection and diagnosis. The suitability of using neural networks as pattern classifiers for power system fault diagnosis is described in detail. A neural network design and simulation environment for real-time FDD is presented. An analysis of the learning, recall and generalization characteristic of the neural network diagnostic system is presented and discussed in detail. >