Renewable energy (RE) generation levels are increasing in modern power systems at a fast rate due to their advantages of clean and non-exhaustible nature of energy. However, this type of generation creates technical challenges in terms of operation and control due to uncertain and un-predictable nature of generation. Islanding is an operational scenario where there is a loss of grid and RE generators continue to feed power to the local load. This has harmful effects on the RE generators and operating personal. Hence, it is expected that islanding scenario is identified in minimum time and RE generators are disconnected within 2s duration after island formation. This paper designed an islanding identification scheme (IDS) by designing a current islanding detection indicator (CIDI) that combines the features computed by processing the current signals, negative sequence current (NSC) and negative sequence voltage (NSV) using the Stockwell transform (ST) and the Hilbert transform (HT). Information contained by the total harmonic distortions of voltage (T HD v ) and current (T HD i ) is also used while designing the CIDI. Islanding and non-islanding events of category-I & II are identified and discriminated from each other by comparison of peak magnitude of CIDI with the first threshold value (FTV) and second threshold value (STV). This IDS effectively recognizes the islanding events even in the noisy environment with minimum non-detection zone (NDZ) and minimum time. The efficiency is greater than 98% even with the noise of 20dB SNR (signal to noise ratio). The performance of proposed IDS is better compared to IDS using discrete wavelet transform (DWT), Empirical mode decomposition (EMD), Slantlet transform & Rdigelet probabilistic neural network (RPNN), and artificial neural network (ANN). The effectiveness of IDS is validated on IEEE-13 nodes test system using MATLAB software, practical distribution network and in real time scenario by use of real time digital simulator (RTDS).
INDEX TERMSHilbert transform; Islanding; Renewable Energy; Stockwell transform; utility grid network.
A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study.
This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India.
This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031.
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