Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks

Abstract

Microgrids can increase power penetration from distributed generation (DG) in the power system. The interface (i.e., the point of common coupling, PCC) between the microgrid and the power utility must satisfy certain standards, such as IEEE Sd. 1547. Energy monitoring of the microgrid at the PCC by the power utility is crucial if the utility cannot install advanced meters at different locations in the microgrid (e.g., a factory). This paper presents a new nonintrusive energy monitoring method using a hybrid self-organizing feature-mapping neural network (SOFMNN). The components of the FFT spectra for voltage, current, kW and kVAR, measured at the PCC, serve as the signatures for the hybrid SOFMNN inputs. The nonintrusive energy monitoring at the PCC identifies different load levels for individual linear/nonlinear loads and output levels for wind power generators in the microgrid. Using this energy monitoring result, the power utility can establish an energy management policy. The simulation results from a microgrid, consisting of a diesel generator, a wind-turbine-generator, a rectifier and a cyclo-converter, show the practicability of the proposed method.

Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses

“…Compared with the traditional load monitoring system, NILM systems offer simple hardware installation. Therefore, an NILM system is a wide load monitoring scheme and a powerful energy management system [3][4][5][6]. Figure 1 shows the overall scheme of an NILM system.…”

Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network

“…Using the 12-bit code of the input signal described in Section 2.2 as an example, if the original code (010100111001b) is changed to (010101011001b), the activated address ( 1 n , 2 n , 3 n ) becomes (9, 5, 5) from (9,3,5), where only 2 n is erroneous, and 1 n and 3 n can be normally outputted. Therefore, the detection method possesses tolerance.…”

“…The training samples are input for quantification, code combination, code classification, and to activate the corresponding memory addresses. Subsequently, the weighting values in the activated addresses are summed and adjusted according to Equation (2). When all the training samples have been applied, an assessment of whether the evaluation value has reached the predetermined error value is conducted.…”

“…During normal operations, the real power and reactive power required by the loads are supplied by the photovoltaic power generation system and utility grid. Consequently, the power at the point of common coupling (P.C.C) is balanced [2]. When the utility grid is disconnected and islanding phenomena is observed, the power at the point of common coupling is unbalanced because the utility grid has ceased to supply power.…”

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

“…Microgrids use techniques with Multi-Agent Systems for island mode operation [6,7] and for strategic control [8]. Similarly, [9] present a new nonintrusive energy monitoring method using ANN. …”