State Estimation of Power Using the Whale Optimization Algorithm

State Estimation of Power Using the Whale Optimization Algorithm

Suvabrata Mukherjee (NSHM College, Durgapur, India) and Provas Kumar Roy (Kalyani Government Engineering College, West Bengal, India)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/IJAMC.2020100109

Abstract

In power systems, the process of attaining a better prediction from a set of variables from state variables is called state estimation (SE). These variables consist of magnitudes of bus voltage and the corresponding angles of all the buses. Because of the non-linearity and intricacy of ever-developing power systems, it has become important to apply upgraded techniques for the dissolution and supervision in practical environments. The discussed analysis evaluates the appositeness of a new metaheuristic technique called the whale optimization algorithm (WOA) which is a population-based algorithm, to reduce the measurement errors so as to gauge the optimal point of the power system when some susceptible values are inadequate. WOA displays admirable attainment in global optimization. It employs a bubble-net hunting approach and it mimics the social behaviour of humpback whales to get the best candidate solution. The approach is tested on IEEE-14, IEEE-30, and IEEE-57 bus test systems and the potency is validated by comparison with the biogeography based optimization algorithm (BBO).
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1. Introduction

The state variables in a power system can be described as dynamic or static. The angular speed of a generator and the rotor angle are treated as dynamic state variables whereas the bus voltage magnitudes and its phase angles are treated as static state variables. For monitoring and control purpose, measurement data with noise must be available to power system operators. Thus, state estimator inputs are corrupt measurement data consisting of active power and reactive power, voltage magnitudes or amperage quantities thereby producing accurate estimate of system state. The state variables to be used for the security analysis application are used in output calculation (Mandloi &, Jain, 2014). Some explicit concerns might occur due to inaccurate state estimation. In case of line overloading, the out-of-bound voltages can be controlled which will facilitate power system operators in locating the issue in the network and necessary rectification measures can be implemented. A block diagrammatic representation of power system state estimation is provided in Figure 1 (Ahmad, Rasool, & Sekar, 2008). Power system state estimation is further classified into Static state estimation (SSE), Dynamic state estimation (DSE) and Tracking state estimation (TSE).

Figure 1.

A block diagrammatic representation of power system state estimation

IJAMC.2020100109.f01

For appropriate functioning of power system, it is crucial for the energy management systems to function properly for real time monitoring and control (Liacco, 1990). So, a state estimation with immense efficiency is obligatory for stable power system operation (Laurenco, Coelho & Bikash, 1990). If a state vector is calculated at any instant of time using the measurement set, then it is termed as SSE.

As said earlier, the state of the power system may not vary much in a small interval of time as it is normally quasi-static in nature. But if there are any changes in load or in case of contingency it is important to monitor the system very closely. So, it is necessary to estimate states at a high frequency rate. But as in the case of SSE if the duration between two estimates is more, it results in a weak co-relation among the states. Thus, in these situations TSE is applied which allows a smooth update for an existing state vector without actually performing the entire SE (Morvaj, 1985).

Even though tracking is an easy way of following the changes in the power system, it does not involve explicit physical modelling (Morvaj, 1985) of the time behavior of the system. To avoid this, Dynamic State Estimators have been developed. Here the physical modelling of dynamic nature of the system is applied. At any certain instant of time with the help of state vector and the physical model of the system, the DSE predicts power system state. Thus, forecasting has many benefits in performing security analysis and the operator gets more time to take preventive measures. Some advantages with this extra step of prediction are as follows:

  • 1.

    Security analysis can be done in advance which gives the operator more time for control in case of emergencies;

  • 2.

    It helps in identifying and rejecting bad data;

  • 3.

    To avoid ill conditioning by replacing pseudo measurements with high quality values;

  • 4.

    Anomalies like Topological errors, changes in the system can be identified.

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