Syed Khaleel Ahmed

Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in peninsular Malaysia to reduce its NTLs in the distribution sector due to abnormalities and fraud activities, i.e., electricity theft. The fraud detection model (FDM) developed in this research study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This approach provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields customer classes which are used to shortlist potential suspects for onsite inspection based on significant behavior that emerges due to fraud activities. Model testing is performed using historical kWh consumption data for three towns within peninsular Malaysia. Feedback from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection indicates that the proposed method is more effective compared to the current actions taken by them. With the implementation of this new fraud detection system TNBDs detection hitrate will increase from 3% to 60%.

Automated breast profile segmentation for ROI detection using digital mammograms

Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the pectoral muscle is an essential pre-processing step in the process of computer-aided detection. Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram. The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram pre-processing. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing and seeded region growing (SRG) algorithm in order to: (1) remove digitization noises, (2) suppress radiopaque artifacts, (3) separate background region from the breast profile region, and (4) remove the pectoral muscle, for accentuating the breast profile region. To demonstrate the capability of our proposed approach, digital mammograms from two separate sources are tested using Ground Truth (GT) images for evaluation of performance characteristics. Experimental results obtained indicate that the breast regions extracted accurately correspond to the respective GT images.

Detection of abnormalities and electricity theft using genetic Support Vector Machines

Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards non-technical loss (NTL) analysis for electric utilities using genetic algorithm (GA) and support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector. This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. GA provides an increased convergence and globally optimized SVM hyper-parameters using a combination of random and prepopulated genomes. The result of the fraud detection model yields classified classes that are used to shortlist potential fraud suspects for onsite inspection. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.

Non-Technical Loss analysis for detection of electricity theft using support vector machines

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards Non-Technical Loss (NTL) analysis for electric utilities using a novel intelligence-based technique, Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behavior. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behavior that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.

A computational intelligence scheme for the prediction of the daily peak load

Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important research area for efficient electricity production. This paper presents a time series approach for mid-term load forecasting (MTLF) in order to predict the daily peak load for the next month. The proposed method employs a computational intelligence scheme based on the self-organizing map (SOM) and support vector machine (SVM). According to the similarity degree of the time series load data, SOM is used as a clustering tool to cluster the training data into two subsets, using the Kohonen rule. As a novel machine learning technique, the support vector regression (SVR) is used to fit the testing data based on the clustered subsets, for predicting the daily peak load. Our proposed SOM-SVR load forecasting model is evaluated in MATLAB on the electricity load dataset provided by the Eastern Slovakian Electricity Corporation, which was used in the 2001 European Network on Intelligent Technologies (EUNITE) load forecasting competition. Power load data obtained from (i) Tenaga Nasional Berhad (TNB) for peninsular Malaysia and (ii) PJM for the eastern interconnection grid of the United States of America is used to benchmark the performance of our proposed model. Experimental results obtained indicate that our proposed SOM-SVR technique gives significantly good prediction accuracy for MTLF compared to previously researched findings using the EUNITE, Malaysian and PJM electricity load datasets.

Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System

This letter extends previous research work in modeling a nontechnical loss (NTL) framework for the detection of fraud and electricity theft in power distribution utilities. Previous work was carried out by using a support vector machine (SVM)-based NTL detection framework resulting in a detection hitrate of 60%. This letter presents the inclusion of human knowledge and expertise into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy IF-THEN rules. The FIS acts as a postprocessing scheme for short-listing customer suspects with higher probabilities of fraud activities. With the implementation of this improved SVM-FIS computational intelligence FDM, Tenaga Nasional Berhad Distributions detection hitrate has increased from 60% to 72%, thus proving to be cost effective.

Image processing of an agriculture produce: Determination of size and ripeness of a banana

Over the last several decades, customers’ lifestyles and needs have gone through tremendous changes. These changes are new challenges for the farmers whose produce has to meet the customers’ demands. The ability to classify agriculture produce based on size and quality is not only going to help the farmer but also the customer. This will help the farmer to grade the fruit, the seller to price it optimally and the customer value for money. Bananas are classified according to sizes, shapes, textures, colors and taste. The process of getting the size and ripeness of a banana is done from its image using the Image Processing Toolbox in MATLAB. These features are important to determine the optimal size and ripeness. From the size, the grade and type of the banana can be determined. The percentage of ripeness can be determined by evaluating the individual pixels of the image.

Agricultural produce Sorting and Grading using Support Vector Machines and Fuzzy Logic

Agriculture sector was accorded a very different treatment in the Ninth Malaysia Plan (9MP) where this sector is being revitalized to become a part of the economic growth engine. The Information and Communication Technology (ICT) application is going to be implemented as a solution in improving the status of the agriculture sector. The idea of integrating ICT with agriculture sector motivates the development of an automated system for sorting and grading of agriculture produce. Currently, the grading is done based on observations and through experience. The developed system starts the grading process by capturing the fruits image using a regular digital camera or mobile phone camera. Then, the image is transmitted to the processing level where feature extraction, classification and grading is done using MATLAB. In this paper, the focus is more on agricultural produce Sorting and Grading technique. The agricultural produce is classified based on fruit shape and size using Support Vector Machines (SVMs) and its grade is determined using Fuzzy Logic (FL) approach. The results obtained are very promising.

NTL detection of electricity theft and abnormalities for large power consumers In TNB Malaysia

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents an approach towards detection of Non-technical Losses (NTLs) of Large Power Consumers (LPC) in Tenaga Nasional Berhad (TNB) Malaysia. The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in Malaysia to reduce its NTLs in the LPC distribution sector. Remote meters installed at premises of LPC customers transmit power consumption data including remote meter events wirelessly to TNB Metering Services Sdn. Bhd. The remote meter reading (RMR) consumption data for TNB LPC customers is recorded based on half-hourly intervals. The technique proposed in this paper correlates the half-hourly RMR consumption data with abnormal meter events. The correlated data provides information regarding consumption characteristics i.e. load profiles of LPC customers, which helps to expose abnormal consumption behavior that is known to be highly correlated with NTL activities and electricity theft. Pilot testing results obtained from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection of LPC customers in peninsular Malaysia indicate the proposed NTL detection technique is effective with a 55% detection hitrate. With the implementation of this intelligent system, NTL activities of LPC customers in TNB Malaysia will reduce significantly.

Fuzzy bang-bang relay controller for satellite attitude control system

Two level bang-bang controllers are generally used in conjunction with the thrust reaction actuator for spacecraft/satellite attitude control. These controllers are fast acting and dispense time dependent; full or no thrust-power to control the satellite attitude in minimum time. A minimum time-fuel attitude control system extends the life of a satellite and is the main focus of this paper. Fuzzy controllers are favored for satellite control due to their simplicity and good performance in terms of fuel saving, absorbing non-linearities and uncertainties of the plant. A fuzzy controller requires a soft fuzzy engine, and a hardware relay to accomplish bang-bang control action. The work in this paper describes a new type of fuzzy controller in which the hardware relay action is configured in the soft fuzzy engine. The new configuration provides fuzzy decision-making flexibility at the inputs with relay like two-level bang-bang output. The new fuzzy controller is simulated on a three-axis satellite attitude control platform and compared with conventional a fuzzy controller, sliding mode controller and linear quadratic regulator. The result shows that the proposed controller has minimum-time response compared to other controllers. Inherent chattering associated with a two-level bang-bang controller produces undesirable low amplitude frequency limit cycles. The chattering can be easily stopped in the proposed fuzzy bang-bang relay controller, hence adding multi-functionality to its simple design.