FHMM Based Industrial Load Disaggregation

A mixed-integer programming approach for industrial non-intrusive load monitoring

Energy consumption data of individual appliances at home can be obtained by nonintrusive appliance load monitoring (NILM) which aims at disaggregating total amount of power consumption into that of each appliance. NILM can be implemented utilizing pattern recognition algorithms, one of which is Factorial Hidden Markov Model (FHMM) that was employed in previous researches. In spite of the known importance of real-time disaggregation system, it has not been actively studied compared to the batch system. This study suggests a prototype of real-time NILM system via ZigBee smart plug and FHMM algorithm.

Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models

Unsupervised approach for load disaggregation with devices interactions

Renewable energy sources are slowly but surely supplementing conventional sources of energy. These energy sources create a large number of micro-grids which become a part of the main grid. Smart meters are essential components in smart micro-grids and smart grids. Together they form a complex system which has become today an attractive area of research world-wide in view of enormous benefits of reliability and safety of power supply economically. Several studies in smart meter applications have shown that in residential buildings, energy consumption can be reduced up to 15% with better management. For this, a non-intrusive appliance load monitoring (NIALM) technique has been advocated which enables appliance identification to help better energy management. The identification is done using load signatures with appropriate algorithm. Several features of load signatures ranging from voltage and current profiles (steady states and transients) to a plethora of features such as active power (P) and reactive power (Q), power factor (pf), harmonic components (h), magnitudes of h (mh), current waveform (CW), eigen values, switching transient waveform, instantaneous admittance waveform, etc have been used in NIALM. The algorithms used vary from a `single algorithm' to `multiple algorithms', the latter used in `Committee Decision Mechanism' (CDM) in which majority of `votes' (i.e., agreement), `cast' by each of the algorithms, decides the final result of appliances identified. This scenario presents a serious difficulty with regard to choice of features and algorithms, besides the possibility of increasing the hardware and software costs of smart meters. The motivation for the study in this paper is provided by this scenario. The basic objective of this paper is to investigate the possibility of including only a limited number of features as load signature of appliances, and the use of only a single algorithm to effectively implement NIALM without any ambiguity. Results of our study so far indicate that generally only four features of P, Q, h and mh are sufficient for detection of appliances in operation; however, in some cases (~>10%), the additional feature, CW, may be needed for final identification.. The experiments conducted so far yielded unambiguous identification of appliances and the results are encouraging enough for possible use in practice for residential loads only.

In this paper, a new method of non intrusive load monitoring (NILM) feature extraction is proposed.The method is based on the analysis and identification of the load transient state and steady state characteristics of the load in the power supply and power outages using the back propagation neural network (BP).The current signal through current sensor is detected in the main switchboard NILM, compared with detecting active power, reactive power and harmonic components of power signals and load the instantaneous change of the state traditional NILM, this new method is more convenient and reduces the amount of computation.The new NILM method integrates artificial intelligence identification technology and load current acquisition technolog.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Hidden Markov models with factored Gaussian mixtures densities

Knowing the power consumption of individual household appliances is useful for end-user as well as utilities. There are two ways for appliance load monitoring (ALM), namely intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). This paper focuses on the NILM approach, and discusses a simple yet effective method to improve its accuracy by constructing a better knowledge-base. The proposed methodology is initially verified with the simulation using the Reference Energy Disaggregation Data (REDD) dataset, and later tested on a lab-scale hardware setup as well. Test results reveal that careful construction of knowledge-base can increase the performance of NILM algorithms. MATLAB is used as the programming platform.

This paper presents a complete real-time implementation of a Non-Intrusive Appliance Load Monitoring (NIALM) system that, is robust under residential voltage level fluctuations. Existing NIALM techniques rely on multiple measurements taken at high sampling rates, but, only have been proven in simulated environments without even considering the effect of residential voltage level fluctuations — which is a severe problem in power systems of most developing countries like Sri Lanka. In contrast, through the NIALM method proposed in this paper, accurate load monitoring results were obtained in realtime using only smart meter measurements taken at a low sampling rate from a real appliance setup under residential voltage level fluctuations. In the proposed NIALM method, initially in the learning phase, a properly constructed MATLABTM Graphical User Interface (GUI) was used to acquire signals of each appliance active power consumption and voltage levels. Then, obtained active power measurements were separated into subspace components (SCs) via the Karhunen Loeve' Expansion (KLE) while also taking the voltage variations into account. Using those SCs, a unique information rich appliance level signature database was constructed and it was then used to obtain the signatures for all possible device combinations. Next, a separate GUI was designed to identify the turned ON appliance combination in the current time window using the pre-constructed signature databases, after reading the total residential active power consumption and the supply voltage. To validate the proposed real-time NIALM implementation, data from a laboratory arrangement consisting of ten household appliances was used. From the results, it was found that the proposed method is capable of accurately identifying the turned on appliances even under severe residential supply voltage level fluctuations.

A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.

<p>The need of electrical energy saving policies is becoming increasingly important nowadays, specially after the latest electricity market tensions in Europe. The energy consumption impacts directly in our electricity bills, thus optimization strategies are required to decrease its negative consequences on the economy and encouraging the consumption of renewable energies and reducing the carbon footprint. For this reason, non-intrusive load monitoring is gaining more attention as a modern strategy to assist in controlling electricity consumption. Several techniques have been proposed using basic information from current or voltage waveforms. However, the use of harmonic features is not widespread due to the need for a device capable of measuring power signals at a high sampling rate and for the high complexity of the disaggregation problem. The use of new high-end IoT devices like openZmeter, can help on this task. In this paper, several sets of harmonic features (current, active power, and custom combination) used along with a particle filter algorithm are compared. The results show that increasing the number of odd harmonics increases the accuracy of the result provided by the algorithm.</p>

This paper proposes a new control sharing method for parallel three-phase inverters in an islanded microgrid. The proposed technique uses adaptive PIDs combined with the communication among the parallel inverters to accurately share active power and reactive power among the inverters via adjusting the desired voltage if there is a distinct difference between line impedance and the load change in the microgrid. Moreover, the paper also presents the response ability of the inverters to maintain the error within the allowed limits as the transmission line is interrupted. The proposed technique has been verified in a microgrid with three parallel distributed generation-inverter units using Matlab/Simulink. In the simulation, as the droop control using the communication information among the inverters, the sharing errors for active power and reactive power are around 0.2% and 0.6%, respectively. As the connection between the microgrid and transmission line is interrupted, the sharing errors for active power and reactive power increase to 0.4% and 2%, respectively. The simulation results have indicated that the proposed technique is superior to the traditional droop control in terms of the accuracy and stability. Therefore, the new proposed technique can be a favor alternative model for active power and reactive power sharing control of parallel inverters in an islanded microgrid.

This paper presents a hybrid method of active power and reactive power control strategy with Quasi Z-source inverter (QZSI) in single phase grid connected photovoltaic (PV) systems. The maximum power from the PV panel is extracted and most of it is supplied as active power to the grid. The major portion of required reactive power exchange to the grid is done by means of thyristor switched capacitor (TSC) and thyristor switched reactor (TSR) to improve the generation capacity of the PV system. In the proposed topology inverter shares the minimum reactive power and large change in reactive power will be delivered/absorbed by the TSC-TSR to get smoother operation in reactive power control. The DC voltage controller and the AC current controller is discussed in brief. The mathematical analysis for grid synchronization method is presented in detail. The control algorithm for active power and reactive power is analyzed and discussed in the paper. The simulation results are shown for validating the proposed concept.

In order to enable demand response schemes for residential and industrial users, it is crucial to be able to predict and monitor each component of the total power consumption of a household or of an industrial site over time. We used the cross-validation method which is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. We exploit Non-Intrusive Load Monitoring (NILM) techniques in order to provide behavior patterns of the variables identified. This work presents a review Non-Intrusive Load Monitoring (NILM) techniques and describe the results of recognition patterns used for the identification of electrical devices. The proposed method has been validated on an experimental setting and using direct measurements of appliances consumption, proving that it allows achieving a high level of accuracy in load disaggregation.