Fault current compensations in resonant grounded distribution systems to mitigate powerline bushfires using a nonsingular terminal sliding model controller

A fault current compensation technique is proposed in this paper for resonant grounded power distribution systems in bushfire prone areas. Arc suppression devices with residual current compensation inverters are used to compensate fault currents due to single line-to-ground faults in order to mitigate powerline bushfires. The main contribution of this paper is the design of a compensation technique for the T-type residual current compensation inverter using a non-singular terminal sliding mode control scheme. The main objective of the proposed scheme is to reduce the fault current and bring its value to a level so that it cannot ignite fires. The proposed controller is designed based on the selection of a sliding surface in a way the singularity problem can be avoided and chattering effects in existing sliding mode controllers can be eliminated. The desired current injection through the residual current compensation inverter is ensured by enforcing the control law into the terminal sliding surface where the control law is determined by satisfying the Lyapunov stability criteria. The performance of the non-singular terminal sliding mode controller is compared with an integral sliding mode controller by considering different values of fault currents where these values are varied by changing fault resistances. Results for simulation in the software and processor-in-loop simulations are verified against operational standards which are essential for mitigating powerline bushfires. This work focuses to design a non-singular terminal sliding mode controller for the residual current compensation inverter which is used in an arc suppression device to compensate both active and reactive components of the fault current and keeps its value below 0.5 A within 2 s after activating the residual current compensation inverter which is a requirement as per the operational standard. This controller is designed based on the selection of a terminal sliding surface while satisfying the condition for avoiding the singularity problem

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architectures. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption and American electric power (AEP) datasets is conducted to analyze the performance of point and probabilistic forecasting methods. The analysis demonstrates higher accuracy of the long-short term memory (LSTM) models with appropriate hyper-parameter tuning among point forecasting methods especially when sample sizes are larger and involve nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of least pinball score and root mean square error (RMSE)

Generalized Dynamical Modeling of Multiple Photovoltaic Units in a Grid-Connected System for Analyzing Dynamic Interactions

This paper aims to develop the generalized dynamical model of multiple photovoltaic (PV) units connected to the grid along with the dynamic interaction analysis among different PV units. The dynamical models of multiple PV units are developed by considering three different configurations through which these PV units are connected to the grid. These configurations include: (a) the direct connection of multiple PV units to the grid; (b) the connection of multiple PV units to the grid through a point of common coupling (PCC); and (c) the connection of PV units without a PCC. The proposed modeling framework provides meaningful insights for analyzing dynamic interaction analysis where these interactions from other PV units are expressed in terms of voltages and line impedances rather than the dynamics of currents. The dynamic interactions among different PV units for all these configurations are analyzed using both analytical and simulation studies. Simulations are carried out on an IEEE 15-bus test system and dynamic interactions are analyzed from the total harmonic distortions (THDs) in the current responses of different PV units. Both analytical and simulation studies clearly indicate that the effects of dynamic interactions are prominent with the increase in PV units

A New Step-Up Switched-Capacitor Voltage Balancing Converter for NPC Multilevel Inverter-Based Solar PV System

This paper proposed a grid connected solar Photovoltaic (PV) Systems with a new voltage balancing converter suitable for Neutral-Point-Clamped (NPC) Multilevel Inverter (MLI). The switched-capacitors used in the proposed converter is able to balance the DC link capacitor voltage effectively by using proper switching states. The proposed balancing converter can be extended to any higher levels and it can boost the DC input voltage to a higher voltage levels without using any magnetic components. This feature allows the converter to operate with the boosting capability of the input voltage to the desired output voltage while ensuring the self-balancing. In this paper the proposed converter is used for a grid connected solar PV system with NPC multilevel inverter, which is controlled using vector control scheme. The proposed grid connected solar PV system with associated controllers and maximum power point tracking (MPPT) is implemented in Matlab/SimPowerSystem and experimentally validated using dSPACE system and designed converters. The simulation and experimental results show that the proposed topology can effectively balance the DC link voltage, extract maximum power from PV module and inject power to the grid under varying solar irradiances with very good steady state and dynamic performances

Nonlinear finite‐time control of hydroelectric systems via a novel sliding mode method

A nonlinear finite-time sliding mode control is proposed in this paper for the governing of complex hydroelectric systems with the finite/fixed setting time. The proposed control method is derived from the finite-time stability and sliding mode control theories. The finite settling time is calculated and bounded, not depending on the initial conditions of the system. The solution trajectory of the controlled hydroelectric system can reach the sliding manifold in a fixed settling time, regardless of initial values. Based on the Lyapunov theory, the controlled hydroelectric system also converges to a reference state within the fixed settling time. A simulation of a high-dimensional hydroelectric system verifies the feasibility of the proposed method. In addition, a comparison between the proposed method and the conventional PID method demonstrates the advantages of the proposed method in the shorter settling time and smaller overshoot. The proposed control method allows for the design of a flexible controller and provides an improvement in dynamic performance

A Bayesian Deep Learning Technique for Multi-Step Ahead Solar Generation Forecasting

In this paper, we propose an improved Bayesian bidirectional long-short term memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation forecasting. The proposed technique applies alpha-beta divergence for a more appropriate consideration of outliers in the solar generation data and resulting variability of the weight parameter distribution in the neural network. The proposed method is examined on highly granular solar generation data from Ausgrid using probabilistic evaluation metrics such as Pinball loss and Winkler score. Moreover, a comparative analysis between MSA and the single-step ahead (SSA) forecasting is provided to test the effectiveness of the proposed method on variable forecasting horizons. The numerical results clearly demonstrate that the proposed Bayesian BiLSTM with alpha-beta divergence outperforms standard Bayesian BiLSTM and other benchmark methods for MSA forecasting in terms of error performance

Design of Nonlinear Backstepping Double-Integral Sliding Mode Controllers to Stabilize the DC-Bus Voltage for DC–DC Converters Feeding CPLs

This paper proposes a composite nonlinear controller combining backstepping and double-integral sliding mode controllers for DC–DC boost converter (DDBC) feeding by constant power loads (CPLs) to improve the DC-bus voltage stability under large disturbances in DC distribution systems. In this regard, an exact feedback linearization approach is first used to transform the nonlinear dynamical model into a simplified linear system with canonical form so that it becomes suitable for designing the proposed controller. Another important feature of applying the exact feedback linearization approach in this work is to utilize its capability to cancel nonlinearities appearing due to the incremental negative-impedance of CPLs and the non-minimum phase problem related to the DDBC. Second, the proposed backstepping double integral-sliding mode controller (BDI-SMC) is employed on the feedback linearized system to determine the control law. Afterwards, the Lyapunov stability theory is used to analyze the closed-loop stability of the overall system. Finally, a simulation study is conducted under various operating conditions of the system to validate the theoretical analysis of the proposed controller. The simulation results are also compared with existing sliding mode controller (ESMC) and proportional-integral (PI) control schemes to demonstrate the superiority of the proposed BDI-SMC

A nonlinear double‐integral sliding mode controller design for hybrid energy storage systems and solar photovoltaic units to enhance the power management in DC microgrids

In this paper, a nonlinear decentralized double‐integral sliding mode controller (DI‐SMC) is designed along with an energy management system (EMS) for the DC microgrid (DCMG). This DCMG includes having a hybrid energy storage system (HESS) that incorporates a battery energy storage system (BESS) and supercapacitor energy storage system (SCESS) while the load demand is met through the power generated from solar photovoltaic (SPV) units. First, dynamical models of each subsystem of DCMGs such as the SPV system, BESS, and SCESS are developed to capture highly nonlinear behaviors of DCMGs under various operating conditions. The proposed nonlinear DI‐SMC is then designed for each power unit in DCMGs to ensure the desired voltage level at the common DC‐bus and appropriate power dispatch of different components to fulfill the load requirement of the DCMG. On the other hand, an energy management system (EMS) is designed to determine the set point for the controller with an aim of ensuring the power balance within DCMGs under various operating conditions where the overall stability is assessed using the Lyapunov theory. Simulation studies along with the processor‐in‐loop validation, including a comparative study with a proportional‐integral (PI) controller, verify the applicability and effectiveness of the EMS‐based DI‐SMC under different operating conditions of the DCMG

Machine Learning to Ensure Data Integrity in Power System Topological Network Database

Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental or deliberate changes in the power system database model. To validate, we have demonstrated the impact of cyber-anomalies on the database model used for operation of energy systems. To counter these cyber-anomalies, we have proposed a defence mechanism based on widely accepted classification techniques to identify the abnormal class of anomalies. In this study, we find that our proposed method based on multilayer perceptron (MLP), which is a special class of feedforward artificial neural network (ANN), outperforms other exiting techniques. The proposed method is validated using IEEE 33-bus and 24-bus reliability test system and analysed using ten different datasets to show the effectiveness of the proposed method in securing the Optimal Power Flow (OPF) module against data integrity anomalies. This paper highlights that the proposed machine learning-based anomaly detection technique successfully identifies the energy database manipulation at a high detection rate allowing only few false alarms

Higher‐order sliding mode current controller for grid‐connected distributed energy resources with LCL filters under unknown grid voltage conditions

The control of the current injected into the grid with lower harmonics is considered as one of the most important issues for the grid integration of distributed energy resources (DERs). The unbalances and harmonics in the grid voltage usually pollute the current injected into the grid due to the power electronic interfaces, for example, inverters. To address such problems, the present paper proposes a nonlinear higher order sliding mode controller (HOSMC) for grid-connected three-phase inverters with LCL filters in order to control the current injected into grid and improve the power quality. The proposed current controller injects the desired current into the grid with lower values of total harmonic distortions (THDs) under any grid voltage condition as well as it reduces the harmonics in the grid voltage. Apart from these, the proposed scheme is developed to provide robustness against parametric uncertainties where these uncertainties are modeled using the Taylor series expansion method. Finally, the performance of the system is evaluated using processor-in-loop (PIL) simulations via MATLAB/Simulink platform through the implementation on a system considering the capacity of the DER as 2 kVA per phase and compared with other existing control strategies