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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.

The model predictive controller exhibits excellent constraint and non-linearity handling capabilities. But still, it is less preferred as a centralized controller in the nonlinear standalone micro-grids for two reasons. One is the computational effort involved in predicting the micro-grid response with a nonlinear micro-grid model. The other one is the involvement of more decision variables in the MPC problem. This paper uses the approximated linear model for the forced response prediction to address the first drawback and solves the nonlinear micro-grid model for natural response prediction. Continuous-time Kautz functions are used in the formulation of the MPC problem to address the second drawback. The Kautz functions approximate the control trajectory of each of the inputs within the control horizon. The approximation decreases the decision variables in the MPC control problem. While doing so, the performance capabilities of the controller can still be conserved.

Dynamic Line Ratings based on thermal limit increases utilization of existing transmission line network. The excess capacity can be used to alleviate congestion, which has positive ramification on system. The computational complexity of dispatch problem increases as temperature and current are related through set of differential equations. Hence, a temporal decomposition based multi-interval ACOPF incorporating dynamic line ratings is proposed to manage strict computational requirements in real time dispatch. The method is tested on 4 bus system and application based on congestion management during real time dispatch and improvement in utilization of wind power based system is proposed.

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.

Among the ancillary service markets for smooth real time operation of the system, reactive power support ancillary service market is regarded as one of the most important services. In this work, the total reactive power required in the system is formulated and based on the bids received from the market participants, value for reactive power is determined. Due to localised nature of reactive power, an assessment of local reactive power requirement is more effective and hence in this work, a localised reactive power market is formulated. The objective of system operator is to minimise value of reactive power in each local market such that optimal scheduling of reactive power generation is achieved subject to system constraints and voltage support. The value function is formulated from the operation cost bids and lost opportunity cost bids (LOC) received from Generation Companies (GENCOs). From the Lagrange function, locational marginal price (LMP) is then determined. The proposed localised reactive power market is tested on IEEE 24-Bus system. It is found that the localised reactive power market maintains sufficient reserve and provide better voltage support. The advantage of proposed pricing scheme is that, the generators are paid based on the value of reactive power requirement in the system determined locally, thereby reducing volatility of price and chance of exercising market power.

Reactive power service is considered as an important ancillary service, due to its contribution towards maintaining system wide bus voltage. In this work, a value based reactive power pricing model suitable for real time market is proposed. In the proposed reactive power market model, bids(operation cost and lost opportunity cost(LOC) of reactive power) are received from the Generating Companies (GENCOs) participating in the real time market. From the bids received, a three component reactive power value function is formulated. The components of value function being load serving component, voltage support component and reserve component of reactive power requirement in the system. The objective of the Independent System Operator (ISO) is to minimise the value function of reactive power requirement in the system subject to generator limits, bus voltage limits and transmission line limits. From the Lagrange function of the optimisation problem, the marginal value (MV) of reactive power is derived to calculate the marginal price (MP) of reactive power. The NLP formulation of the market model is solved using DICOPT solver in GAMS and anlaysed on IEEE 24-Bus system. The simulation results prove that the proposed algorithm is efficient in clearing the reactive power market in such a way that the system wide bus voltage deviation is minimal and sufficient reactive power reserve is maintained in the network.

Electric vehicles are an integral part of futuristic smart grids. Electric vehicles give rise to a new player in the retail market called as aggregator. This paper proposes an intelligent charging scheduling problem for an Electric Vehicle (EV) aggregator considering vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capabilities with an objective to minimize the total charging cost. Since electricity price at the charging node may be subject to uncertainties, Information Gap Decision Theory (IGDT) is proposed in this paper to handle uncertainties in the price. The original intelligent charging scheduling problem is non-linear. The paper proposes a modified Mixed Integer Linear Programming (MILP) based reformulation and solves with CPLEX using GAMS as an aggregator.

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.

Under deregulation, Automatic Generation Control (AGC) scheme may consider economic dispatch of Generating Companies (GENCOs) as well in addition to minimising the system frequency deviation. This will result in increased participation from GENCOs in AGC. GENCOs being private utilities, will have the private information like cost function etc which will not be shared with the system operator. This imposes a challenge on implementing classical economic dispatch problem in AGC scheme. The contribution of the paper is a game theoretic based model of AGC scheme that will ensure an optimal dispatch to competing GENCOs. This is achieved by defining an optimal participation factor for GENCOs that will minimize their cost of production. A game theoretic approach towards AGC as a dynamic game is presented and GENCOs are considered as Reinforcement learning agents. A Single Agent Q-Learning method along with pursuit algorithm is used at each GENCO agent to achieve the equilibrium point in AGC. The algorithm was studied with three competing GENCOs. Results show the suitability of the proposed AGC model and its ability to minimize the deviation in system frequency while ensuring economic operation of the system.

The work proposed in this article proposes a different energy management scheme for residential consumers having higher penetration of solar power and heating loads. The presented methodology assumes the heating loads present in the residence as non-critical loads and tries to operate them in power saving mode, whenever necessary. In the considered scheme non-critical loads are connected in series with AC electric springs which in turn schedule them as per the requirement. The major scope of this work is to make the non-critical loads consume lesser power by pressing springs into action in the presence of solar power. It is expected that the springs compliment the efforts of solar panels in reduction of power consumed from utility grid, daily peaks and energy purchase from the utility grid. In order to implement the proposed methodology a completely customer driven home energy management algorithm is proposed and the robustness of the same is tested on a residential consumer, considering a 24 load curve. The simulation studies for the projected methodology are performed in MATLAB.

This paper analyses the benefit of a multi-stage transmission expansion planning (TEP) over single stage TEP. The paper establishes the conditions (load growth and interest rate) under which the two formulations are equivalent, and when a multi-stage TEP formulation could lead to a better solution. The paper answers an important question of how the planning span shall be divided into stages when going for a multi-stage TEP. The paper further analyses the effect of high peak to average load ratio (PAR) in a multi-stage TEP. The single-stage and multi-stage TEP problems are formulated to minimize the total investment and operation cost over the planning period. The operating cost is calculated by dispatching the generators and loads in a double sided auction market where the supply and demand bids are assumed to be fixed over the planning period. The TEP problem has a mixed integer linear programming (MILP) formulation and is tested on 6 bus Garver System implemented in GAMS.

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.

The work presented in this paper proposes a new Demand side management (DSM) algorithm for residential users having higher penetration of heating/resistive loads. The proposed algorithm uses electric springs to operate the non-critical loads in power saving mode, so as to reduce the daily peak, ramping and energy bill paid by the consumers. In order to implement the suggested methodology it is assumed that all the non-critical loads present in the residential complex are fitted with electric springs. The quantum of the power to be saved is completely left to the consumers and the customers are given full independence to choose the required percentage saving in power, which in turn reduces the electricity bill paid by them. Further it should be noted that the proposed energy management scheme re-shapes the entire load curve and achieves energy saving without shifting the non-critical appliances to off-peak timings, which is something new to the literature. The robustness of the suggested algorithm is tested by considering a 24 hour load curve of a residential user and the simulation studies of the same are performed in MATLAB.

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.

Non-consistency of energy availability from Renewable Energy Sources needs estimation and scheduling in advance so that the other certain sources of energy like fuel cells, diesel generators, storage devices etc., can be scheduled appropriately to maintain load-generation balance in real time. Evolutionary program techniques are proving handy and reliable in the process. This article uses an Artificial Fish Swarm algorithm to solve the problem of day-ahead scheduling of generation in a mix of Renewable Energy Sources, despatchable sources and storage. The utility function of hourly generation cost is considered for optimization along with various microgrid operational constraints. The performance of the algorithm is validated by applying to schedule generation in a microgrid in grid connected mode consisting of one wind turbine and one PV source as Renewable energy sources, one diesel generator and fuel cell as despatchable generators and a battery for storage. The scheduled generation of each generator, power exchange of storage source along with its state of charge are evaluated for optimum cost of generation.

The work presented in this paper addresses the problem of main grid dependability in micro grids using Electric springs. The micro grids which have more penetration of renewable sources are bound to depend on main grids during generation intermittence of the micro sources. In order to reduce the dependability of micro grids on main grid an efficient energy management algorithm has been presented. The proposed algorithm schedules the non-critical loads present in the micro grid using A.C Electric springs and the simulation studies are performed in MATLAB. The reduction in energy consumption from the main grid is achieved by making the non-critical loads consume lesser power during generation intermittence in the micro grid.

Islanding detection is required for both microgrids and individual distributed generations (DGs). Several methods exist in literature but they are with some limitations. In this paper a new scheme is proposed which doesn't fall under any of the existing methods. Cross coupling in control action between the components I d (direct axis component) and I q (quadrature axis component) is introduced by a novel method which makes the system unstable in islanded mode. The coupling is such a way that it does not adversely affect the control system dynamics in grid connected mode. The proposed method does not induce power quality problems and has no Non-Detection Zone (NDZ). Small signal stability analysis and Simulink simulation are performed to find the effectiveness of this scheme for a worst case scenario.

Advanced Metering Infrastructure (AMI), which is being considered as the first step towards smart grid, has been well acknowledged by distribution utilities for automating the management, operation and control of low voltage distribution systems with multifold installations happening across various countries. AMI offers smart meters, variety of protocols, sensors, network and software infrastructure providing two-way communications between utility & consumer. With the emergence of new system level concept in the smart distribution arena, Distribution Automation (DA) and AMI systems convergence, for developing next generation advanced distribution automation applications it is required to change the way in which AMI systems are currently being operated and provide additional capabilities or enhancements for integrated operation with DA system. In this paper we analyze the requirements for such AMI system based on state-of-the art technology advancements towards effective DA-AMI convergence operation along with a case study.

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.

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 unit commitment problem. The use of augmented Lagrangian technique by convergence of decomposition method was proposed in 1994, and in 2007 chance constrained optimization was used for providing a solution to the stochastic unit commitment problem. Dynamic Programming is a conventional algorithm used to solve deterministic problem. In this paper DP is used to solve the stochastic model. The stochastic modeling for generation side has been formulated using an approximate state decision approach. The programs were developed in MATLAB environment and were extensively tested for 4 unit 8 hour system. The results obtained from these techniques were validated with the available literature and outcome was satisfactory. The commitment is in such a way that the total cost is minimal.

Smart Grid envisions improving the efficiency, reliability, quality, security, economy etc. of the electric energy systems to meet the 21st century demands. As the major concern in 21st century is the environmental impact, it is very essential to make the present grid eco-friendly. This paper presents the research carried out in these issues and also on the latest technologies in smart grid. Smart Grid features such as self-healing, eco-friendliness, are presented in this paper, with detailed discussions on substation automation, demand response, and distributed energy resources

This paper propose a new decaying DC component Estimation algorithm for digital relaying. Fault currents tend to include a DC decaying component. This component decreases the accuracy and speed of the protection relay operation. The proposed algorithm can estimate and eliminate the DC decaying component from fault current signals after one cycle from the fault instant. Also, it can be applied to a conventional discrete Fourier transform to calculate phasor quantities of fault currents in a digital protection relay. In the proposed algorithm, the DC decaying magnitude and time constant are estimated exactly by integrating fault currents during one cycle. The DC decaying component is eliminated by subtracting the DC value at each sampling instant. To verify the performance of the proposed algorithm, we performed a DC component estimation test and distance protection test using PSCAD/EMTDC. The results of the PSCAD/EMTDC simulation showed that the proposed algorithm can estimate DC components exactly from fault currents and can be applied to digital protection relays for phasor extraction

Smart grid enables active participation by consumers which is known as Demand Response. In order to match the power demand and power supply, a new method for Dynamic Demand Control (DDC) with AGC in Smart Grid Environment is proposed. A load frequency control using DDC based on price signals was modeled in this study. When frequency decreases customers are charged for Unscheduled Interchange. The frequency deviation was compared with the frequency deviation obtained when generation control, using PI controller, alone was implemented for frequency control. Thus DDC alone is sufficient to maintain the system frequency, during small load variations. DDC will play a major role in reducing these losses caused to the GENCOs under a Smart Grid environment.

Smart grid enables active participation by consumers in Demand Response. In order to match the power demand and power supply, Dynamic Demand Control (DDC) with AGC in Smart Grid Environment is used. A load frequency control using DDC, was modeled for a two area system in this study. The frequency deviation and tie line flow deviation was compared with the deviations obtained when generation control, using PI controller, alone was implemented for frequency control. The PI controller parameters in the new scheme are then optimized using Lyapunov technique. Thus DDC alone is required to maintain the system frequency, during small load variations. DDC will play a major role in the economic operation of GENCOs under a Smart Grid environment.

This paper gives a solution for the unit commitment problem (UCP) for an power system to minimize the total production cost over a period of time by considering the load as a random(Probabilistic) variable instead of a deterministic one. The UCP is a mixed integer large scale non linear program. If the number of unit is more and the time horizon is large, then the analysis become more complex. That is the reason still researches are active in finding the solution for unit commitment problem. In this paper the proposed methodology by considering the load as a probabilistic variable instead of regular deterministic one. By considering the load in this way we can solve the problem of UCP as a real time problem. In the proposed methodology LR method is used to separate the problem in to a primal and dual problem and each sub-problem is solved by using dynamic programming. The economic dispatch problem is solved by particle swarm optimization. By comparing the results with conventional method the proposed methodology gives the schedule of generator with minimum production cost.

This paper proposes a parallel processing approach to contingency analysis (CA) for power system security evaluation. Full AC power flow analysis is done for each contingency and violations of bus voltages and line flows are evaluated. All possible line outages are considered to avoid overlooking of certain critical cases. Parallel Processing is employed to increase the speed of execution. Parallelism is achieved by sharing each contingency across processors. The proposed methodology is implemented in a Linux Cluster. Data communication is performed through the Message Passing Interface (MPI). The effectiveness of parallelism is demonstrated by performing contingency analysis on standard IEEE Systems. The performance of the algorithm is analyzed in terms of computation speed and efficiency in comparison with the sequential approach.

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.

A new object-oriented design (OOD) for unbalanced distribution system is proposed and employed for developing short circuit analysis software. The difficulty associated with the procedure-oriented implementation of short circuit analysis program is overcome with a new object-oriented implementation. The purpose of using object-oriented methodology (OOM) for unbalanced distribution system is to achieve a flexible software model for the complex nature of the system involving three phase, two-phase and single-phase components. Considerable reduction in the programming complexity can be obtained by the proposed method. The method was successfully tested on benchmark systems like IEEE 13-bus and IEEE 34-bus systems.

This paper develops optimal bidding strategy for operating multi-unit pumped storage power plant in day-ahead electricity market. Based on forecasted hourly market clearing price, a multistage looping algorithm to maximize the profit of multi-unit pumped storage plant is developed considering both spinning and non-spinning reserve bids and meeting the technical operating constraints. The proposed model is adaptive for the nonlinear three-dimensional relationship between the power produced, the energy stored, and the head of the associated reservoir. Evolutionary tristate particle swarm optimization (ETPSO) based approach is also proposed to solve the same problem, combining basic particle swarm optimization (PSO) with tri-state coding technique and mutation operation. The discrete characteristic of a pumped storage plant is modeled using tri-state coding technique and genetics based mutation operation is used for faster convergence in getting global optimum. The proposed approaches are applied with an actual utility consisting of four units. Experimental results for different operating cycles of the storage plant indicate the attractive properties of the ETPSO approach in a practical application, namely, a highly optimal solution and robust convergence behaviour.

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.

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.

Optimum location and size of capacitors for a distribution system under APDRP (Accelerated Power Development Programme) is presented. In the present study capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Genetic algorithm is used as an optimization tool, which obtains the optimal values and location of capacitors and minimizes the objective function, which is the power loss in the distribution network under study. A dedicated distribution system load flow is used to calculate power loss and voltage profile of the distribution system. Implementation aspects and important results for a 33 bus, 29 Indian power distribution system and practical 34 bus system have been presented to highlight the working of the algorithm.

This paper proposes a method of solving transmission network expansion problem using LP-Based particle swarm optimization. An optimal expansion plan for the existing transmission network is proposed. It formulates the design requirements of network planning as an operational planning model and is solved by an optimization algorithm such that an optimal planning scheme is obtained satisfying all constraints. Particle swarm optimization is a population-based search algorithm. The experimental results for linear programming technique, LP-based PSO and DC model are given.

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.

Distribution system has been analyzed in this paper using object-oriented approach. The important contribution of this paper is the development of software objects for various distribution system components in such a way that they can be reused in most of the distribution system analysis programs. The design, proposed in this paper, is used for developing load flow analysis program. Object-oriented design replicates the physical system structure exactly in the software. The extensibility of the object-oriented design is exploited to extend the radial load flow analysis module for performing the load flow analysis of weakly meshed system by deriving few specialized objects from the fundamental objects used for radial load flow. Modeled objects are implemented in C++, an object-oriented programming language, and tested with various test systems. The results obtained for 69-bus radial system and 33-bus weakly meshed system are provided. This design is being extended for developing load flow analysis module for 3-phase unbalanced distribution system and distribution system with dispersed generations.

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.

This paper presents an efficient and reliable evolutionary based approach to solve the economic load dispatch (ELD) with security constraints. The proposed approach employ particle swarm optimization (PSO) algorithm for ELD. Incorporation of type 1 PSO as a derivative-free optimization technique in solving ELD with voltages and lineflow constraints significantly relieves the assumptions imposed on the optimized objective function. The proposed approach has been tested on three representative systems, i.e. IEEE 14 bus, IEEE 30 bus and IEEE 57 bus systems respectively. The feasibility of the proposed method is demonstrated and the results are compared with linear programming, quadratic programming and genetic algorithm respectively. The developed algorithms are computationally faster (no. of load flows) than the other methods.

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. >

Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission system.

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. >