A.K. Sinha

Congestion Management Using Multiobjective Particle Swarm Optimization

Summary form only given. This paper presents an effective method of congestion management in power systems. Congestions or overloads in transmission network are alleviated by generation rescheduling and/or load shedding of participating generators and loads. The two conflicting objectives (1) alleviation of overload and (2) minimization of cost of operation are optimized to provide Pareto-optimal solutions. A multiobjective particle swarm optimization (MOPSO) method is used to solve this complex nonlinear optimization problem. A realistic frequency and voltage dependent load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics is used to solve this problem. The proposed algorithm is tested on IEEE 30-bus system, IEEE 118-bus system, and Northern Region Electricity Board, India (NREB) 390-bus system with smooth as well as nonsmooth cost functions due to valve point loading effect.

A wavelet multiresolution analysis for location of faults on transmission lines

Faults on EHV lines are quite common. They cause disruption in power supply. Accurate location of faults will result in faster maintenance and restoration of supply. This paper presents a new method for the location of faults based on wavelet multiresolution analysis (MRA). EMTP (Microtran) is employed to generate the time domain input signal. Daubechies eight (D-8) wavelet transforms of the three phase currents on transmission lines from both the ends are used. The effects of fault inception angle and fault impedance are examined. Extensive simulation work has been carried out and results indicate that the proposed method is very effective in locating the fault with a high accuracy.

Dynamic state estimator using ANN based bus load prediction

This paper presents an algorithm for dynamic state estimation of a power systems. The method uses ANN based bus load prediction for the prediction step in the DSE. The proposed DSE uses rectangular coordinate formulation for measurement equations. A second order dynamic state estimator which incorporates the full nonlinearities of the measurement function is used for the filtering step. The inclusion of nonlinearities makes the proposed state estimator perform better in case of sudden large changes in load/generation.

Power System Real-Time Monitoring by Using PMU-Based Robust State Estimation Method

Accurate real-time states provided by the state estimator are critical for power system reliable operation and control. This paper proposes a novel phasor measurement unit (PMU)-based robust state estimation method (PRSEM) to real-time monitor a power system under different operation conditions. To be specific, an adaptive weight assignment function to dynamically adjust the measurement weight based on the distance of big unwanted disturbances from the PMU measurements is proposed to increase algorithm robustness. Furthermore, a statistical test-based interpolation matrix H updating judgment strategy is proposed. The processed and resynced PMU information are used as priori information and incorporated to the modified weighted least square estimation to address the measurements imperfect synchronization between supervisory control and data acquisition and PMU measurements. Finally, the innovation analysis-based bad data (BD) detection method, which can handle the smearing effect and critical measurement errors, is presented. We evaluate PRSEM by using IEEE benchmark test systems and a realistic utility system. The numerical results indicate that, in short computation time, PRSEM can effectively track the system real-time states with good robustness and can address several kinds of BD.

Identification of Catastrophic Failures in Power System Using Pattern Recognition and Fuzzy Estimation

This paper presents a new approach for finding the sequence of events that may lead to catastrophic failure in a power system. The probable sequences (of events) leading to catastrophic failures are identified using risk indices which incorporate the severity as well as the probability of the contingencies. Probable collapse sequences are identified offline for different possible loading conditions using a modified fast decoupled load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics and stored in a knowledge base. Pattern recognition method and fuzzy estimation are used for online identification of collapse sequences for any operating condition from the stored database (knowledge base).

Real-time hybrid state estimation incorporating SCADA and PMU measurements

This paper proposes a novel hybrid state estimation method using traditional SCADA (Supervisory Control And Data Acquisition) and newly deployed limited PMU (Phasor Measurement Unit) measurements. System states are estimated when a set of SCADA and/or PMU measurements come in. As PMU measurements come much faster (typically one sample in 20ms) than SCADA measurements (typically one sample in 10 seconds), in between two SCADA measurements, system states of PMU unobservable buses are interpolated using an interpolation matrix (H)live PMU measurements. In between two SCADA samples, if PMU measurements change significantly, pre-computed interpolation matrix (H) is compensated with a sensitivity change matrix (ΔH) and system states are estimated using the corrected interpolation matrix. In order to compute the ΔH, the method classified the measurement set into four sub-sets i.e. PMU measurements, SCADA measurements of PMU boundary buses with significant change, SCADA measurements adjacent to the selected boundary buses, and remaining SCADA measurements and run a modified weighted least square method with different weights corresponding to each sub-set of measurements. This compensation improves the estimation accuracy significantly. Effectiveness of the proposed scheme is evaluated on a number of IEEE benchmark test systems and evaluation results are presented in this paper.

A wavelet multiresolution-based analysis for location of the point of strike of a lightning overvoltage on a transmission line

Lightning flashes on extremely high voltage lines are quite common and can lead to insulation breakdown or other line hardware failure causing disruption in supply. An accurate location of the site of failure will result in faster maintenance and restoration of supply. In this paper, a novel technique based on the wavelet multiresolution analysis (MRA) for locating the point of strike of a lightning overvoltage on a transmission line is presented. Lightning strikes at different points on a 400-kV transmission line of different line lengths are simulated by shifting the point of strike of the lightning impulse every 5 km. The Electromagnetic Transient Program (EMTP) [Microtran] is employed to generate the time-domain input signal. Daubechies eight (D8) Wavelet transform is used to analyze lightning overvoltages. The tenth level output of MRA detail signals extracted from the original signals is used as the criterion for the analysis. Extensive simulation work has been done and results indicate that the proposed scheme is very effective in locating the point of strike with reasonable accuracy.

Simulation and Analysis of Acoustic Wave Propagation due to Partial Discharge Activity

Acoustic detection of partial discharges is based on the retrieval and analysis of mechanical signals produced by partial discharges. Acoustic method is widely used in locating partial discharge sources in transformers. For source location constant velocity of acoustic signals is used. Acoustic method also has potential to classify the partial discharges for better assessment of insulation condition. This paper presents a computer simulation of acoustic signal and analysis of its propagation behavior. It is found that acoustic velocity is not constant over distances of practical interest. An algorithm proposed for source location with distance dependent acoustic velocity leads to reduction in location error. Acoustic pulse produced by partial discharges is deformed and attenuated while propagating through transformer to sensor. This change depends on propagation distance and medium. So, frequency spectrum of sensor output AE pulse is not true representation of source pulse frequency spectrum. Partial discharge classification based on the output acoustic signal can lead to wrong classification. Knowing the location of partial discharge source, frequency dependent attenuation characteristics and output acoustic signals frequency spectrum, an estimation of input pulse frequency spectrum and its parameters is made for the classification of partial discharges

Power system restoration: planning and simulation

This paper describes a restoration guidance simulator, which allows power system operator/planner to simulate and plan restoration events in an interactive mode. The simulator provides a list of restoration events according to the priority based on some restoration rules and list of priority loads. It also provides in an interactive mode the list of events, which becomes possible as the system grows during restoration. Further, the selected event is validated through a load flow and other analytical tools to show the consequences of implementing the planned event.

Power system security assessment using pattern recognition and fuzzy estimation

Abstract A simple, fast, efficient and reliable method for power system security assessment is presented. The method is based on pattern recognition and fuzzy estimation techniques. The security status of a power system operating condition is recognized from the stored knowledge about similar operating conditions. The method is able to classify the system operating condition into secure/insecure state; and for the insecure state it provides information about pertinent contingencies which may cause insecurity. The knowledge about the system operating conditions (patterns) are stored in a structured memory by grouping similar patterns into clusters which are arranged into a hierarchical tree structure. This enables a very fast two level search for the near neighbours of the input pattern. The security status of the input pattern is determined using a fuzzy estimation technique. This not only provides a very reliable security classification, but the fuzzy grade membership also provides a quantitative ‘level of confidence’ for the security classification. Digital simulation results exhibiting the characteristics of the proposed method are presented.